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Full text of "Relationship between health related behaviors and health status among minority populations"

THE RELATIONSHIP BETWEEN HEALTH RELATED BEHAVIORS AND HEALTH 
STATUS AMONG MINORITY POPULATIONS 



UiP 



By 

DELIA F. OLUFOKUNBI 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 

OF THE REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 

UNIVERSITY OF FLORIDA 

1997 



C'ii^/- ' ^:^>'a 



I would like to dedicate this dissertation to my mother, Storm Somers. It is 
through her love, guidance, and support over the years that I have found the strength and 
motivation to pursue my dreams. She is truly the "wind beneath my wings." 



ACKNOWLEDGMENTS 
First and foremost, I would like to express my deepest gratitude to my chair and 
mentor. Dr. Suzanne B. Johnson. Her wisdom, guidance, and unwavering encouragement 
over the years have provided me with the skills and supportive enviromnent to achieve 
my dream. Through her example, I have seen rewards of personal integrity, commitment, 
and respect and, as a result, have grown tremendously, both professionally and personally. 

Second, I would like to thank Dr. Michael Miller for his invaluable guidance on 
this project. He provided me with the knowledge and resources necessary to complete 
this project and his wonderftil personality has made even the rough times seem bearable. 
I have learned a great deal through my interactions with him and I am ver>' grateftil. 

Third, I would like to thank all my committee members for their time and 
assistance in the development and completion of this project. Their collaborative insight 
helped to make this study the best it could be and also led to some very lively and quite 
interesting meetings. I would also like to thank Dan Nissen for all his assistance in 
helping me sort through this database and confront my most feared enemy, SAS. 

Finally, I would thank my family and friends who have seen me through the best 
and worst of times. Their love and unconditional support have kept me sane over the 
years. 



Ill 



TABLE OF CONTENTS 

PAGE 



ACKNOWLEDGMENTS iii 

LIST OF TABLES vi 

ABSTRACT viii 

CHAPTERS 

1 REVIEW OF LITERATURE 1 

Introduction 1 

Methodological Issues 3 

Definition of Minority 3 

Problems with Measurement 5 

Minority Health Status and Health Related Behaviors 6 

Access to Care/Utilization of Health Care Services 8 

Health Related Behaviors 10 

Theoretical Concepts of Minority Health Status 18 

Health Status, Socioeconomic Status, and Health Insurance: 

An interdependent relationship 20 

Health insurance coverage 20 

Socioeconomic status 23 

National Medical Expenditure Survey 27 

Policy Implications 30 

Purpose of Research "..'.!.'.... ^' 31 



IV 



2 METHOD 34 

Data and Sample — 34 

Measures 36 

Dependent Variables 40 

Missing Data 41 

3 RESULTS 43 

Descriptive Analysis 44 

Multivariate Analysis 54 

Overall Health Rating 56 

Role Functioning 58 

Physical Funtioning 60 

Acute Symptoms scale 62 

Chronic Symptoms scale 63 

Medical Conditions scale 64 

Supplemental Analysis 72 

4 DISCUSSION 84 

Relationship between Race/ethnicity and Health Related Behaviors 86 

Relationship between Race/ethnicity and Insurance Coverage 87 

Relationship between Race/ethnicity and Dependent Variables 89 

Limitations 94 

Future Research 97 

LIST OF REFERENCES • ^ 99 

BIOGRAPHICAL SKETCH 108 



LIST OF TABLES 
Table ^. _. . page 

1 . Leading Causes of Death by Gender and Race 11 

2. Rate ratio of age-adjusted death rates from 15 leading causes 

of death, by sex and race - United States, 1992 13 

3 . Percentage Distribution and Test of Significance of Demographic Variables, 

Insurance Status, and Health Related Behaviors by race/ethnicity 48 

4. Percentage Distribution and Test of Significance of Outcome Variables 

by Race/ethnicity 49 

5. Percentage Distribution and Test of Significance of Medical Conditions 

Scales by Race/ethnicity 50 

6. Correlation Coefficients for Demographic Variables 51 

7. Correlation Coefficients for Health Related Behavior Variables 52 

8. Correlation Coefficients for Health Outcome Variables 53 

9. Summar> of Hierarchical Regression Analysis for Variables Predicting 

Overall Health Rating 66 

1 0. Summar>' of Hierarchical Regression Analysis for Variables Predicting 

Role Functioning 67 

1 1 . Summary of Hierarchical Regression Analysis for Variables Predicting 

Physical Functioning 68 

12. Logistic Regression Models Predicting Acute Symptoms 69 

1 3 . Logistic Regression Models Predicting Chronic Symptoms 70 

14. Logistic Regression Models Predicting Medical Conditions 71 



VI 



15. Summary of Hierarchical Regression Aiialysis for Variables Predicting 

Health Insurance Coverage 77 

1 6. Summary of Hierarchical Regression Analysis for Variables Predicting 

Body Mass Index 78 

1 7. Summary of Hierarchical Regression Analysis for Variables Predicting 

» . Smoking Index 79 

1 8 . Logistic Regression Models Predicting Physical Exercise 80 

1 9. Logistic Regression Models Predicting Blood Pressure Check 81 

20. Logistic Regression Models Predicting Wearing Seat-belt 82 

21. Summary of Variables g3 



vu 



Abstract of Dissertation Presented to the Graduate School 

of the University of Florida in Partial Fulfillment of the 

Requirements for the Degree of Doctor of Philosophy 

THE RELATIONSHIP BETWEEN HEALTH RELATED BEHAVIORS AND HEALTH 
STATUS AMONG MINORITY POPULATIONS 

By 

Delia F. Olufokunbi 

December, 1997 

Chairman: Suzanne B. Johnson, Ph.D. 

Major Department: Department of Clinical and Health Psychology 

Research in the area of health care has systematically found increased rates of 

morbidity and mortality among various minority groups compared the White population. 

Specific causal explanations for these differences have included genetic factors, 

differences in socioeconomic status, increased exposure to environmental hazards, and 

differences in insurance coverage. However, one area that has received relatively little 

attention is the influence of health related behaviors on the health status of minorities. 

Using data from the 1987 National Medical Expenditure Survey, this study examines the 

effect of health related behaviors and insurance coverage on health status across three 

racial/ethnic groups (Whites, Blacks, and Hispanics). Results found significant 

differences across racial/ethnic groups on health status measures. Specifically, on the 

outcome measures of Health Rating, Role Functioning, and Physical Functioning, Blacks 

reported the worst overall functioning while Hispanics reported the best overall 



vui 



functioning. In addition, Hispanics reported the least amount of acute symptoms, chronic 
symptoms, or medical conditions while Blacks and Whites were not different in their 
report of symptoms on these three scales. Inclusion of socio-demographic variables, 
health related behaviors, and insurance coverage to the predictive model resulted in 
Blacks actually report better role and physical functioning compared to Whites. In 
addition. Blacks report significantly less acute and chronic symptoms and medical 
conditions compared to Whites. Results support hypothesis that differences in health 
related behaviors between Whites and racial/ethnic minorities influence overall health 
status and point to the importance of considering preventive behavioral health care as a 
public health priority. 






% 






IX 



CHAPTER 1 
INTRODUCTION 



The status of health care in the United States has become one of the most daunting 
social issues in recent years. While the nation's health care system is the most expensive 
in the world, the comparative health care status of American citizens ranks lower than 
other western, industrial countries on a number of indicators (Nickens, 1995; Vandenbos, 
1993). With the spiralling costs for health care and the approximately 36 million 
Americans who are uninsured, "health care reform" has become one of the most 
emotionally charged expressions within the social and political arena (Bingaman, Frank. 
&. Billy, 1993; Kerrey & Hofschire, 1993). The twofold policy goal of health care reform 
is overall cost control with increased access to quality services. In addition, health care 
outcome has become an important issue in assessing the relationship between cost and 
quality. The exact mechanism by which to achieve these goals has been an area of 
significant controversy. In fact, one of the most vehemently debated social issues facing 
the U.S. Congress presently is the restructuring of the national Medicare and Medicaid 
public health insurance system. 

With the heightened awareness of the current state of health care in the United 
States, concerns regarding the status of minority health as a public health issue will need 
to be addressed. It is estimated that by the year 2050, approximately 50% of the United 



States population will be of minority background. VVTiiie there is great diversity across 
and within minority groups, the overall health status of minorities compared to the white 
population is poor (Manton, Patrick, & Johnson, 1987; Nickens, 1991, 1995; Williams, 
Lavizzo-Mourey. & Warren, 1994). Research evaluating differential health status, access 
to care, service utilization, and health perceptions across racial/ethnic groups have found 
large disparities (Manton, Patrick, & Johnson. 1987; Davis, Lillie-Blanton, Lyons, 
Mullan, Powe. & Rowland, 1987; Yergan, Flood, LoGerfo, & Diehr. 1987; Martin, 
Perez-Stable, Marin, Sabogal, & Otero-Sabogal, 1990; Strogatz, 1990; Cornelius, 1991, 
1993; Bernard. 1993; Lillie-Blanton. Martinez. Taylor, &. Robinson, 1993; Farraro, 1993; 
Ford & Cooper, 1995). The risk factors attributed to this differential health status include 
differences in lifestyle and health behavior (e.g., alcohol, smoking, and nutrition), health 
consequences associated with low socioeconomic status (e.g.. economic barriers to access 
to health services, a lack of health insurance due to chronic unemployment, stress), 
inadequate knowledge of health practices, more hazardous occupations and exposure to 
environmental pollution, and genetic factors (e.g., sickle cell trait) (Manton, Patrick, & 
Johnson, 1 987; Robinson, 1 984). However, specific causal explanations for these 
discrepancies are difficult to differentiate given the high correlation between race and 
other socioeconomic variables (i.e, income, education, occupation, culture, environment, 
health insurance coverage) (Schulman. Rubenstein, Chesley, & Eisenberg). 

In this era of rapid changes in the health care industr>' and increased attention to 
health outcome in relation to health care costs, behavioral and epidemiological research 
can inform policy decision-makers of the special health and medical needs of minority 
populations and the specific variables that may mediate these needs. With this broad goal 



in mind, the present study sought to expand upon and further illuminate the relationship 
between race/ethnicity and health status. Specifically, given the interrelationship 
between race and certain socio-demographic variables as well as the potential influence 
of health insurance coverage and differential attitudes towards health care and health 
insurance on health status, this study sought to examine the strength of the relationship 
between race/ethnicity and health status after controlling for the influence of specific 
socio-demographic variables, health related behaviors, health insurance coverage, and 
attitudes towards health care and health insurance. 

Methodological Issues 
Definition of Minoritv 

There has been substantial controversy over the use of racial/ethnic classifications 
and the meanings associated with these classifications. Membership in particular 
racial/ethnic groups assumes the experience of a common heritage and life experience 
(Lillie-Blanton, Martinez, Taylor, & Robinson, 1993). Although there is considerable 
heterogeneity within the American culture, there are four generally recognized minority 
groups in the United States: African Americans, Asian and Pacific Islanders, Latinos, and 
Native Americans (Nickens, 1991). There are also significant cultural and 
socioeconomical variations within these minority groups. For example, although 
Mexican Americans and Vietnamese Americans have low mean family incomes, Cuban 
Americans and Japanese Americans have comparably high mean family incomes 
(Nickens, 1995). In addition, although persons from the Caribbean Islands and Haiti may 
consider themselves African Americans for the purpose of completing survey 



questionnaire data, there are significant cultural differences between these groups and 
other African Americans. As such, evaluations involving broad racial/ethnic categories 
can run the risk of improper generalization and create a distorted view of actual 
racial/ethnic status. Unfortunately, most large scale studies observing racial/ethnic 
differences have focused primarih on broad categories of classification and within group 
data are limited. 

In addition, the rationale behind racial/ethnic categorizations may prove 
problematic when conceptualizing broad social issues such as health care and methods for 
addressing these issues. Specifically, research on racial differences in health status has 
historically been dominated by a genetic model that defines race as reflecting biological 
homogeneity and differences in health outcome across groups as largely genetically 
determined (Williams. Lavizzo-Mourey. &. Warren, 1994). On the other hand, racial 
variations across groups may actually indicate differential exposure to behavioral, 
psycho-social, material, and environmental risk factors that are more reflective of 
socioeconomic rather than genetic differences across groups (Williams. Lavizzo-Mourey. 
& Warren, 1994). Lillie-Blanton et al. (1993) argue for the use of racial/ethnic 
classifications as a measure of particular sociocultural experiences rather than a biological 
inheritance marker. Ultimately, despite concerns regarding risks of generalization, it is 
important to understand the underlying mechanisms behind racial/ethnic differences on 
measures of health status and health outcome so remedies can be developed to 
specifically address these mechanisms. 



Problems with Measurement 

Measurement is another important methodological issue, particularly when 
dealing with large sur\'ey data. Problems inherent in measuring health status may 
exaggerate or mask actual differences between groups of interest (Andersen. Mullner, & 
Cornelius, 1987). 

Unreliable estimates may be obtained when the sample size is small (Andersen, 
Mullner. & Cornelius. 1987). Even in large surveys, sampling may result in a small 
minority representation. In an aUempt to address this problem, the National Medical 
Expenditure Survey oversampled specific populations of interest, including African 
Americans, Hispanics, the poor, and the elderly. 

Some have argued that self-report measures of health status offer the least 
objective assessment of health status (Andersen, Mullner, & Cornelius, 1987). Refusal 
rates, lack of adequate recall, misinterpretation of questions, and provision of inaccurate 
but socially acceptable responses may contribute to systematic but extraneous differences 
among groups. For example, in a review of national databases, Anderson, MuUer, and 
Cornelius (1987) found Blacks to exhibit the greatest health deficits based on objective 
measures (mortality rates, clinical examinations) but better health status on self-report 
measures of illness conditions, symptoms, and restricted activity days. 

As such, while this evaluation will explore variation in health status across 
minority groups, it will be important to keep in mind the limitations in generalization 
given the broad variations within racial/ethnic groups and the inherent limitations of self- 
report data. 



'f-.-^ r-.-.r 



fhm-:: r,i I'rirn^j^ 



Minority He alth Status and Health Related Behaviors 

Differences in the leading causes of death for racial/ethnic groups provide 
information about the differential risk factors experienced across these groups. These 
types of data have important policy implications in terms of developing preventative 
health programs and addressing the specific needs of particular groups. Table 1 presents 
data from the Centers for Disease Control and Prevention on the leading causes of death 
m the United States according to gender and race status for 1992. Table 2 illustrates the 
ratio of age-adjusted death rates from 15 leading causes of death by sex and race. In most 
cases, age-adjusted death rates were higher for African Americans compared to White 
Americans. The greatest differences were found in the rates of death by homicide 
(African American death rates were 6.5 times that of White Americans) and by HIV 
infection (African American death rate was 3.7 times that of White Americans). Chronic 
obstructive pulmonar}' diseases and allied conditions and suicide were the only two 
conditions for which the death rate for African Americans was lower than that for White 
Americans (CDC, 1 994b). 

,. Nickens ( 1 995) reviewed mortality data from a number of national databases. He 
found an exceedingly high death rate among young Native Americans which could be 
attributed to intentional and unintentional injuries (i.e., injuries related to alcohol use and 
the direct health effects of alcohol use). Among Hispanics, death rates tended to be 
similar to Whites. Asian/Pacific Islanders had lower death rates than Whites for all age 
groups. Nickens (1995) also examined racial/ethnic group differences on the six major 
causes of death identified by the^l985 Health and Human Services Secretary's Task Force 



, . ( 



on Black and Minority Health: cancer, cardiovascular disease, chemical dependency, 
diabetes, infant mortality, and homicide. African Americans were found to have higher 
death rates than White Americans for all of the six categories and HIV infection. 

Schwartz. Kofie, Rivo, and Tuckson ( 1 990) evaluated differential mortality rates 
for 12 separate diseases (tuberculosis, cervical cancer, Hodgkin's disease, rheumatic 
disease, hypertensive heart disease, acute respiratory disease, pneumonia, and bronchitis). 
Overall, they found a 4.5-fold excess mortality among African Americans compared to 
White Americans. African Americans had significant elevations in the mortality rates for 
eight of the 12 causes evaluated. 

These findings were hypothesized to reflect either a higher incidence of disease in 
Afi-ican Americans or a higher case fatality rate due to advanced stage of disease at time 
of diagnosis, co-morbidity, or delays in obtaining adequate treatment. Utilizing national 
data sources, Andersen. Mullner, & Cornelius (1987) compared the health status of 
African Americans and White Americans using measures of death, disease, disability, 
discomfort, and dissatisfaction. They found that for the most objective measure of health 
status (i.e. death) and the most subjective measure (i.e., dissatisfaction), African 
Americans had poorer health status. However, self-report of acute conditions tended to 
be higher for Whites than for African Americans for all age groups. Specifically, they 
found the death rate among Blacks greater for all of the most common causes of death: 
heart disease, cancer, stroke, accidents, and homicides. Less serious acute conditions 
(i.e., minor injuries, colds) were more often reported by Whites than Blacks. The authors 
caution that the use of self-report indices of health status may mask actual differences in 
health status in terms of mortality rates. Observing the rate of "excess deaths" (the 



difference between the actual number of deaths in a minority population and the number 
of deaths that would have occurred if the mortality experiences of that group were the 
same as among the White population), the Secretary's Task Force on Black and Minority 
Health Report found 42.3 percent excess deaths among African Americans. 14 percent for 
the Spanish surnamed population of Texas, 2 percent among Cuban-bom persons. 7.2 
percent for those Mexican-bom, 25 percent for American Indians (in Williams, Lavizzo- 
Mourey,& Warren. 1994). ' \ '■ '•-'■-* 

Nickens (1991) also reported Native Americans have high excess death rates 
(22%) and 87% of those excess deaths occurred before age 45. High levels of alcohol 
abuse, suicide, and unintentional injuries, and interpersonal violence are thought to be 
responsible. In 1992, the ratio of infant mortality among African Americans was 2.4 
times that of Whites (National Center for Health Statistics. 1995). This race differential 
has remained fairly constant despite an overall pattern of decline (Manton. Patrick, & 
Johnson, 1987). Manton, Patrick, and Johnson ( 1 987) point to socioeconomic 
differences between African American and White American mothers that increase risk 
factors such as poorer prenatal care, poor nutrition, and higher rates of teenage pregnancy, 
However, in a review of the literature, Nickens (1995) found differences in infant 
mortality to persist after controlling for the effects of social class, prenatal care, and 
living conditions. 
Access to Ca re/Utilization of Health Care Services. 

The extent to which individuals have access to primary health care and are able to 
utilize that care m an effective mamier can have profound effects on overall health status 



and mortality rates (Franks, Clanc}. Gold. & Nutting, 1993; Hadley, Steinberg. & Feder, 
1991; Patrick, Madden. Diehr, Martin, Cheadle, & Skillman, 1992; Weissman. Stem, 
Feilding, & Epstein, 1991). Access to and utilization of health care are often determined 
by various factors including whether an individual has a usual source of care, the type of 
source of that care, and the availability and convenience of the care (Cornelius. 1991). 
Cornelius ( 1 99 1 ) examined the use of ambulatory and inpatient medical care by 
white and African Americans under the age of 65 who experienced an episode of illness. 
The study found African Americans more likely to be poor, uninsured, unemployed or 
disabled so as to prevent them from working, and in fair or poor health. In accounting for 
observed racial differences in the use of medical care, the analysis found health status, 
age, income, insurance coverage, and usual source of care to be more significant 
determinants of differences than race. The author notes that differences in access to care 
may reflect the fact that African Americans are more likely to fall into the groups that 
experience disparity in access to care (i.e., low-income, uninsured, no usual source of 
care). 

Enactment of Medicaid and Medicare in 1965 paved the way for increased access 
to health care services for low-income, elderly, and ethnic minorities in the United States. 
However, universal insurance coverage and greater access may not eliminate the racial 
disparities in health status. Miller and Curtis (1993) point to the Medicare program as an 
example of increased access not necessarily correlating with increased utilization. They 
cite a 1992 report to Congress in which the Physician Payment Review Commission 
demonstrated significant problems for African American Medicare beneficiaries in 
accessing health care services. 



^r !;^ > ■'' 10 



Health Related Behaviors 






Lifestyle choice and health related behaviors often reflect sociocultural patterns 
and economic resources and have a profound affect on health outcome. Lillie-Blanton et 
al. (1993) suggest that the health profiles of racial/ethnic minorities too often include risk 
factors that have been established in the literature to be associated with specific states of 
ill-health and disease and are modifiable. For example, obesity and being overweight, 
excessive tobacco and alcohol consumption, drug abuse and related behaviors (i.e., needle 
sharing, prostitution), stress, and interpersonal violence have all been associated with 
decreased health status and increased mortality (Manton, Patrick, & Johnson, 1987; 
Lillie-Blanton et al.. 1993; Nickens, 1991; Taylor, 1990). 

Obesitv/overweipht. A strong association has been established between obesity 
and the prevalence of diabetes, hypertension, and breast and uterine cancer in women 
(Lillie-Blanton et al.. 1993; Clark & Mungai, 1997; Trentham-Dietz et al.. 1997). Blacks 
and Hispanic are significantly more likely to be overweight compared to Whites (Myers 
et al., 1995; Clark & Mungai, 1997). Analyzing data from the National Health and 
Nutrition Examination Survey II (NHANES: 1976-1980) and the Hispanic Health and 
Nutrition Examination Study (HHANES 1982-1984), Lillie-Blanton et al. (1993) found 
twice as many African American women (44.4 percent) were overweight compared to 



11 



Table 1 LEADING CAUSES OF DEATH BY GENDER AND RACE 



All Races 



1 

2 
3 
4 
5 
6 
7 
8 
9 
10 



Diseases of heart 

Malignant neoplasms 

Cerebrovascular diseases 

Chronic obstructive pulmonary diseases 

Unintentional injuries 

Pneumonia and influenza 

Diabetes Mellitus 

HIV infection 

Suicide 

Homicide and legal intervention 



White Males 

1 Diseases of heart 

2 Malignant neoplasms 

3 Cerebrovascular disease 

4 Unintentional injuries 

5 Chronic obstructive pulmonary 
diseases 

6 Pneumonia and influenza 

7 Suicide 

8 HIV infection 

9 Diabetes Mellitus 

10 Chronic liver disease and 

cirrhosis 



White Females 



1 


Diseases of heart 


2 


Malignant neoplasms 


3 


Cerebrovascular disease 


4 


Chronic obstructive 




pulmonary diseases 


5 


Pneumonia and influenza 


« 


Unintentional injuries 


7 


Diabetes mellitus 


8 


Atherosclerosis 


9 


Nephritis, Nephrotic syndrome, 




and nephrosis 


10 


Septicemia 



Black Males 

1 Diseases of heart 

2 Malignant neoplasms 

3 Homicide and legal intervention 

4 HIV infection 

5 Unintentional injuries 

6 Cerebrovascular diseases 

7 Pneumonia and influenza 

8 Chronic obsnuctive pulmonary 
disease 

9 Certain conditions originating 

in the perinatal period 

10 Diabetes Mellitus 





Black Females 


1 


Diseases of hean 


2 


Malignant neoplasms 


3 


Cerebrovascular diseases 


4 


Diabetes mellitus 


5 


Unintentional injuries 


6 


Pneumonia and influenza 


7 


Certain conditions originating 




in the perinatal period 


8 


HIV infection 


9 


Chronic obstructive pulmonary 




diseases 


10 


Homicide and legal 




intervention 






OA*^'^. ,■ 



i.,rf 



12 



Table 1 —continued 



American Indian/Alaskan Native Males 

1 Diseases of heart 

Unintentional injuries 
Malignant neoplasms 



10 



Chronic liver disease & cirrhosis 

Suicide 

Cerebrovascular diseases 



7 Diabetes mellitus 

8 Homicide and legal intervention 



Pneumonia and influenza 

Chronic obstructive pulmonary 
diseases 



American Indian/Alaskan Native Females 

1 Diseases of heart 

2 Malignant neoplasms 
Unintentional injuries 
Diabetes mellitus 
Cerebrovascular diseases 
Chronic liver disease & 
cirrhosis 

Pneumonia and influenza 
Chronic obstructive pulmonary 
diseases 

Nephritis, nephrotic syndrome, 
& nephrosis 
Congenital anomalies 



J 
4 
5 
6 

7 
S 



10 



Asian/Pacific Islander Males 

1 Diseases of heart 

2 Malignant neoplasms 

3 Cerebrovascular diseases r 

4 Unintentional injuries 

5 Pneumonia and influenza 

6 Chronic Obstructive pulmonary 
diseases 

7 Homicide and legal intervention 

8 Suicide 

9 Diabetes Mellitus 

10 HIV infection 



Asian/Pacific Islander Females 

Malignant neoplasm 
Diseases of heart 
Cerebrovascular diseases 
Unintentional injuries 
Pneumonia and influenza 
Diabetes mellitus 



1 
2 

3 
4 
5 
6 



7 Chronic obstructive pulmonary 

diseases 
9 Suicide 

9 Congenital anomalies 

10 Nephritis, nephrotic syndrome & 
nephrosis 



Hispanic Male 

1 Diseases of heart 

2 Malignant neoplasms 

3 Unintentional injuries 

4 HIV infection 

5 Homicide and legal intervention 

6 Cerebrovascular diseases 

7 Chronic liver disease and 
cirrhosis 

8 Suicide 

9 Congenital anomalies 

10 Nephritis, nephrotic syndrome, 
and nephrosis 



Hispanic Females 

Diseases of heart 
Malignant neoplasms 
Cerebrovascular diseases 
Diabetes mellitus 
Unintentional injuries 
Pneumonia and influenza 
Certain diseases originating in 
the perinatal period 

8 Chronic obstructive pulmonary 
diseases 

9 Diabetes mellitus 

10 Pneumonia and influenza 



1 

2 
J 
4 
5 
6 
7 



Health, United States (Sources: Center for Disease Control and Prevention, National Center for Health 
Statistics: Vital Statistics of the United States. Vol. II, Mortality, Part A) 



13 



Table 2. Rate ratio of age-adjusted death rates from 15 leading causes of 

death, by sex and race - United States, 1992 



Rank Cause of Death 

1 Diseases of heart 

2 Malignant neoplasms 

3 Cerebrovascular disease 

4 Chronic obstructive pulmonar>' disease 

5 Accidents and adverse effects 
Motor vehicle accidents 
all other accidents and adverse effects 

6 Pneumonia and influenza 

7 Diabetes mellitus .. . ., 

8 HIV infection 

9 Suicide 
'0 Homicide and legal intervention 
11 Chronic liver disease & cirrhosis 
'2 Nephritis, nephrotic syndrome & 

Nephrosis 

13 Septicemia 

14 Atherosclerosis 
Certain conditions originating in 

the perinatal period 12 

(based on infant mortalit> rate) 



15 



MaJe.-Female 


Black:White 


1.9 


1.5 


1.5 


1.4 


1.2 


1.9 


1.7 


.8 


2.6 


1.3 


2.3 


1.0 


3.0 


1.6 


1.7 


1.4 


1.1 


2.4 


• 7.0 


3.7 


4.3 


.6 


4.0 


6.5 


2.4 


1.5 


1.5 


2.8 


1.3 


2.7 


1.3 


1.1 



All Causes 



3.2 



1.7 1.6 



From Center for Disease Control (1994b). Mortality patterns - United States. 1992. MM WR, 43(49). 916-920 



14 

White American women (23.9 percent). Latino women also had higher percentages of 
being overweight; however, there was some variation by subethnic group (Mexican 
Americans, 41.6 percent; Puerto Ricans, 40.2 percent; and Cuban Americans, 31.6 
percent). 

Using the 1991 and 1992 Behavioral Risk Factor Surveillance System (BRFSS). a 
study by the CDC attempted to describe the prevalence of certain risk factors (cigarette 
smoking, sedentarv' lifestyle, and overweight) for chronic disease among racial/ethnic 
groups. African American and American Indian/Alaskan Native women more frequently 
reported being overweight (38% and 30% respectively) compared to White American 
(21.7%). Asian/Pacific Islander (10.1%) and Hispanic (26.5%) women. The prevalence 
of overweight in men was highest for American Indians/Alaskan Natives (34%) and 
lowest for Asian/Pacific Islanders (11%). African American and White American men 
had relatively equivalent rates of being overweight (CDC, 1994a). 

Diabetes is the second highest cause of adult death among African Americans. 
The higher prevalence of obesity among black women and differential management of the 
disease has been linked to the increased rate of diabetes in African Americans (Manton, 
Patrick, & Johnson. 1987). Diabetes is also noted to disporportionately affect the 
Hispanic population (Zambrana & Ellis, 1995). 

Physical activity. Regular physical activity has been found to have a significant 
impact on health. The positive effects of physical activity range from preventing and/or 
managing hypertension, heart disease, diabetes, osteoporosis, and obesity to lowering 
rates of cancer and stroke (Healthy People, 2000, 1992). A study looking at the 
relationship between physical activity levels and coronary heart disease and death found 



that moderate levels of leisure time physical activity were associated with 63% less 
cardiovascular deaths and 70% less total deaths than the low leisure time physical activity 
group (Leon et al., 1987). Despite the positive effects associated with regular physical 
activity, the literature indicates that both Black and Hispanic are less likely to engage in 
regular physical exercise compared to Whites (Myers et al..l995. Lockery and Stanford, 
1995). 

Tobacco. According to a report by the Center for Disease Control, an estimated 
89.8 million (49.8%) adults in the United States have smoked at some point in their lives. 
Of these, 46 million (25%) were current smokers, 20.4% were daily smokers, and 4.6% 
were occasional smokers (CDC, 1994c). Tobacco has been cited as one of the most 
preventable causes of mortality in the United States (Nickens, 1991). Data from the 1993 
National Health Interview Survey (NHIS-2000) were used to determine the prevalence of 
smoking among adults (CDC, 1994c). American Indians/Alaskan Natives and African 
Americans were found to have the highest rates of smoking, 38.7 percent and 26.0% 
percent respectively. Prevalence of smoking was lowest among Hispanics (20.4) and 
Asian/Tacific Islanders (18.2%). Significant sex differences existed across all race/ethnic 
groups with men reporting a higher rate than women, except for American Indian/Native 
Alaskan. Interestingly. American Indian/Native Alaskan women (40%) have the highest 
smoking rate across race/ethnicit>' and sex categories. Prevalence differences were also 
related to income level and education level. Persons living below the poverty level had 
higher smoking rates than those living at or above the poverty level. Smoking prevalence 
was lower among persons with less than 8 years of education compared to those with 9- 



f 

:.■ ^:'." 16 



15 years of education. However, prevalence \aried inversely with education le\el among 
persons with 9 or more years of education (CDC. 1994c). 

Data from the 1991/1992 Behavioral Risk Factor Surveillance System also found 
the highest percentage of cigarette smoking among American Indians/Alaskan Native 
men and women (CDC. 1994a). Marin. Perez-Stable. Marin. Sabogal, & Otero-Sabogal 
(1990) found Hispanics to report smoking significantly less than non-Hispanic whites. 

Alcohol use. Cirrhosis, which is often associated with excessive alcohol 
consumption, is twice as high in African Americans as ^^^lite Americans (Manton, 
Patrick, & Johnson. 1987). In a review of the literature on alcohol consumption. Bernard 
(1993) found no significant differences between reported drinking patterns among 
African Americans and White Americans. However, he found differemial age effects in 
how African Americans and White Americans consumed alcohol. Between 14-17 years 
of age, African American youths tend to drink significantly less than White American 
youth. However, this gap gradually decreases with age as older African Americans report 
higher rates of alcohol consumption than younger African Americans and older White 
Americans report lower rates than younger White Americans. Manton. Patrick, and 
Johnson (1987) note that while there are more alcohol abstainers among Blacks, there are 
also more heavy consumers and that it may this continued high alcohol consumption in 
adulthood in certain Black subpopulations that led to the high incidence of cirrhosis in 
Blacks. 

HIV infection. Data from the Center for Disease Control presem some startling 
figures on the effect of HIV infection on the national death rate statistics. In 1992, HIV 
infection was the overall leading cause of death among men aged 25-44 years and the 






i • 



«._>.''* T . ^. 1 



17 

fourth leading cause of death for women within that age range. The rate of AIDS and 
HIV infection varied widely across minority populations. When stratified by race, age 
(25-44), and sex, HIV is the leading cause of death for African American men (25.3% of 
total deaths) and Hispanic men (24.1% of total deaths), the second leading cause of death 
for White American men (1 8.5% of total deaths), the sixth leading cause of death among 
Asian/Pacific Islander men (8.8% of total deaths), and the sixth leading cause of death 
among American Indians/Alaskan Native men (4.5% of total deaths). For this age 
bracket, HIV is the second leading cause of death among African American women 
(16.5% of total deaths), the third leading cause of death among Hispanic women (12.4% 
of total deaths), the sixth leading cause of death for White American women (3.8% of 
total deaths), the seventh leading cause of death among American Indian/Alaskan Native 
women (1 .9% of total deaths), and the ninth leading cause of death for Asian/Pacific 
Islander women (1.1% of total deaths). Mortality data for Hispanic men and women are 
from 1991 because data from 1992 were unavailable at the time of the report (CDC, 
1993b). 

In 1992, the death rate from HIV infection for persons aged 25-44 was three times 
as high for African American men (136 per 100,000) compared to White American men 
(42.1 per 100,000). Among young women, the African American/White American 
differentials are even greater. Death rates among young African American women (38 
per 100,000) are 12 times those of young White American women (3.3 per 100,000) 
(CDC, 1993b). 

^'°^^"^^- Wi^h increasing attention being focused on health related behaviors and 
its influence on overall health status, homicide and interpersonal violence is an increasing 



18 

public health concern (Nickens. 1991). The largest difference in mortalit} between 
African Americans and White Americans is in the rate of homicide (Manton. Patrick, & 
Johnson, 1987). The ratio of homicide rates for African Americans are currently 7 and 
4.2 times those for White men and women respectively (Nickens, 1991). 

Theoretical Concepts of Minority Health Status 

Williams, Lavizzo-Mourey. and Warren (1994) present a model illustrating the 
complex relationship between race and health status (Figure 1 ). The model illustrates the 
interrelationship among macrosocial factors (i.e.. historical conditions, economic 
structures, and political order), racism (i.e., prejudice and discrimination), and geographic 
origin/biological factors (i.e., physiological, morphological, and genetic factors). Within 
the model, macrosocial factors are thought to create racism, placing particular emphasis 
on selected physical characteristics or geographic origins of specific groups. Racism 

Other models look specifically at the effects of social and environmental 
conditions on minority health outcome (Nickens, 1995; Lillie-Blanton et al.. 1993; 
Zambrana. 1988). For example. Lillie-Blanton et al. (1993) point to the importance of 
considering social environmental conditions as a significant determinant of health. Social 
conditions (i.e.. type and place of employment and place of residence) can have a direct 
effect on health status in terms of exposure to occupational and environmental hazards. 
Social environmental conditions can also have an indirect effect through their influence 
on other determinants of health such as adjusting to life stress, lifestyle behaviors, access 
to health care and utilization of health service. Evaluating the relationship between health 
and social conditions among Latino and African American women, Lillie-Blanton et al. 



19 



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(1993) note that Latino and African American women have lower median incomes than 
White American women. They point to the negative influence of inadequate financial 
resources, limited education, and the stress of living in densely populated inner cities on 
health status. Zambrana (1988) also argues that theoretical models that attempt to 
interpret differential health outcomes of minority populations take into account the 
interaction of socioeconomic, racial, cultural, and regional variables on health care 
access, health status, chronic life stress, sources of social support, and work history. 

Nickens (1995) notes the fact that despite similar overall poverty rates among 
Hispanics and African Americans, Hispanics tend to have significantly better health 
status than African Americans. He proposes that the effect of intergenerational poverty, 
systematic opposition, and frustrations related to perceived continual discrimination may 
have a indirect effect on health status and mortality rates among specific minority 
populations such as African Americans and American Indians. 

Health Status. Socioeconomic Status, and Health Insurance: 

An Interdependent Relationship 
Health Insurance Coverage. 

Approximately 50% of African Americans and Latinos under 65 are uninsured or 
covered by public insurance (i.e., Medicaid) compared to 20% of White Americans. In 
addition, of this population. 15% of White Americans, 25% of African Americans, and 
35% of Latinos are uninsured (Nickens, 1991). Cornelius (1993) used data fi-om the 1987 
National Medical Expenditure Survey to characterize the various barriers to medical care 
for White American, African American, and Hispanic children. She found 14% of White 



21 

American children were uninsured, compared to 22% of African American children, and 
33% of Hispanic children. She also found uninsured children were twice as likely as 
children with insurance to have no usual source of health care. Her analysis further 
indicated that having health insurance reduced the disparities in use of health care 
services across groups. However, disparities still existed by type of insurance in terms of 
where children went for care, likelihood of having a regular doctor, and convenience of 
obtaining care. Valdez et al. (1993) used data from the 1980 and 1990 Current 
Population Surveys to examine determinants of health insurance coverage for Latinos. 
They found 39% of Latinos under 65 years of age were uninsured for the entire year of 
1 989. They also found health insurance coverage among Latinos varied substantially. 
Mexican Americans and Central and South Americans experienced approximately twice 
the uninsured rate as Puerto Ricans and Cuban Americans. Valdez et al. (1993) 
hypothesize that the disparities in the rates of insurance coverage are largely a result of 
primary employment in lower skilled and lower paid sectors of the economy, which are 
less likely to provide employment benefits such as insurance. 

Members of minority groups are dramatically overrepresented among those on 
public insurance programs (Kasper & McMillan, 1986; Nickens, 1991). As such, a brief 
overview of such programs, specifically Medicaid, is important for this review. Medicare 
(Title XVIII) and Medicaid (Title XIX) were established by U.S. Congress in 1965 with 
the enactment of the Social Security Act Amendments (DeLeon et al., 1992). While 
Medicare served to provide medical care to the elderly and disabled regardless of income, 
Medicaid was designed to assist states in providing health care specifically for the poor 
and categorically needy. The two major groups eligible for Medicaid fall into two 



22 

programs: Aid to Families with Dependent Children (AFDC) and Supplemental Security 
Income (SSI). Medicaid is coordinated on both the federal and state level and provides 
medical assistance to individuals and families with low income and resources. Operating 
within broad federal guidelines, states have the authority to set eligibility standards and 
types of services covered by medicaid as well as rates of payment for services and the 
duration and scope of these services. 

Given the large proportion of minorities who are poor, it was believed that 
Medicaid would reduce the disparities in access to health care experienced by minorities. 
In fact, Davis et al. (1987) indicate that racial differences in use of health services 
decreased substantially between 1965 and 1980. This decrease is largely attributed to the 
introduction of Medicaid, Medicare, and other public health programs. However, a 
number of problems exist with Medicaid that affect access and utilization of health care 
services, including limitations in the extent of coverage, low reimbursement fees resulting 
in limited participation of health care providers, and poor quality of service received by 
Medicaid beneficiaries (Davis et al.. 1987). In addition, different eligibility standards and 
levels of coverage across states result in differential access and utilization of health care 
services by state. As Valdez et al. (1993; p.889) summarize, "The categorical nature of 
the Medicaid programs, which serve only a fraction of the nation's poor, and the 
inadequate medical resources available through these programs further reduce the 
access." 

Despite the noted problems intrinsic to this system of public insurance. Medicare 
and Medicaid combined currently account for nearly 90% of federal health care costs. 
Over the years, federal expenditures for Medicare and Medicaid have grown by 



23 

exorbitant rates. For example, federal health care costs grew by 20% between 1987 and 

1989 (Broskowski. 1991). A report by the Congressional Budget Office estimates that by 
the year 2002, 25% of all federal expenditures will be devoted solely to Medicare and 
Medicaid (Bingamin, Frank, & Billy, 1993). Payments for Medicaid between 1980 and 

1990 grew from $14 billion to $41 billion and, by 1996, federal expenditure is projected 
to exceed $120 billion (DeLeon et al., 1992). As such, state Medicaid agencies are 
currently facing critical challenges as federal mandates for Medicaid coverage expand, 
the number of eligible recipients increase, and the cost of delivering health care services 
in this country escalates rapidly. On both the national and state level, efforts are 
underway to reform the Medicaid system. However, reform efforts may have potentially 
drastic effects for minority populations. Efforts to manage and improve the Medicaid 
system must take into account the specific health needs and health related problems of the 
populations most likely to be effected by the changes. In addition, as Medicaid funding 
for the delivery of health care services is curtailed, more attention will have to be focused 
on preventative health behaviors. 

Socioeconomic Status 

Literature evaluating the effect of socioeconomic status on differential health 
outcomes across racial/ethnic groups have produced somewhat conflicting results. 
Socioeconomic status is usually measured by combinations of occupation, income, and 
educational attainment (Nickens, 1991). On these three variables, minorities tend to be 
lower compared to Whites (Nickens, 1995). Poverty rates for African Americans and 
Hispanics was 33.3% and 29.3% respectively in 1992, compared to 1 1.6% for Whites 



24 

(National Center for Health Statistics, 1994). Native American poverty rates are noted to 
be similar to that of Latinos and African Americans (Nickens, 1991). 

Pappas, Queen. Hadden, and Fisher (1983) examined trends in mortality across 
socioeconomic groups using data from the 1 986 National Mortality FoUowback Survey 
and the 1 986 National Health Interview Survey. They found an inverse relationship 
between mortality rates and socioeconomic status for both African Americans and White 
Americans. However, higher mortality rates were found for African Americans 
compared White Americans for every age group and income or educational level. Sorlie, 
Rogot, Anderson, et al. (1992) examined mortality rates for African Americans and 
White Americans by family income using data from the National Longitudinal Mortality 
Study. They also found lower mortality rates for higher income individuals for both 
African Americans and White Americans. However, death rates were higher for African 
Americans than White Americans within each level of income. 

Otten, Teutsch, Williamson, & Marks (1990) followed up participants from the 
National Health and Nutrition Examination Study (NHANES). They were interested in 
determining the effects of six known risk factors (smoking, blood pressure, body-mass 
index, diabetes, alcohol use, and cholesterol level) and income on the Black- White 
differential in mortality. They found the unadjusted mortality ratio for African 
Americans verses White Americans was 2.3 for adults aged 35-54. This ratio decreased 
to 1 .9 when the six risk factors were adjusted and to 1 .4 when adjusted for the risk factors 
and family income level. Thus, approximately 69% of the excess mortality could be 
accounted for by the six known risk factors and family income. These findings provide 



25 

some evidence for the association between socioeconomic status, prevalence of risk 
factors, and the higher mortality rate among African Americans. 

Bernard (1993) conducted a review of medical literature from 1987-1991 on the 
health status of African Americans. He found a higher prevalence of malignancies, 
diabetes, hypertension, obesity, homicide, and unintentional injuries compared to White 
Americans. When socioeconomic status and educational level were controlled, he found 
racial differences decreased or disappeared for some of the conditions. However, no 
mention is made within the review of exactly how socioeconomic status was defined or 
measured or what other variables were included in the analysis of these studies. Blendon 
et al. (1989) analyzed data from a 1986 national survey on access and found disparities 
for racial/ethnic minorities persisted even after controlling for socioeconomic status. 
Bassett and Krieger (1986) analyzed the influence of race and social class on 
breast cancer survival rates in a population-based sample. After adjusting for social class, 
age, and other medical predictors of survival, the authors found that black- white 
differences in breast cancer survival rates diminished greatly. However, the data the 
authors used in the analysis had no direct indicators of social class. As such, they 
measured social class by using each patient's address to determine their census block 
group characteristics and then calculated each block group's social class composition by 
determining the proportion of residents who could be classified as "working class." 
Using such global measures of social class presents considerable concern regarding 
conclusions based on this definition. .^ 

Markides and Coreil (1986) coin the term "epidemiological paradox" in 
describing the relationship between SES and Hispanic health status. The overall health 



26 

status of Hispanics as a group is good and similar to that of the White population, despite 
the fact that the overall poverty rate among Hispanics and African Americans is similar. 

In an attempt to account for the differential health status of African Americans 
and Hispanics given the similar poverty rates among the two groups, Nickens (1995) 
proposes considering the differential effects of perceived poverty, systematic oppression 
and acculturation across minority groups in the United States. He further points out that 
low education and low family income may be necessary but not sufficient conditions for 
poor health status among minority populations and suggests the need to explore other 
causal factors that might mediate the relationship between health status and SES and 
mortality rates, such as, perceived powerlessness, frustration, and negative self-image. 

For example, he notes. 

Despite the importance of socioeconomic status on health status, when the relative 
health status of minority populations is examined, it does not simply correlate 
with their socioeconomic status. Socioeconomic status may operate in minority 
populations with a time component. It may be that populations that have been 
poor in the United States over several generations without substantial progress up 
the socioeconomic ladder, suffering continual discrimination and frustration, are 
likely to feel much more powerless and will have a very different perception of 
their lot than newly arrived immigrants who are poor but still hopeful. (Nickens 
1995,p.l52) 

In addition, as mentioned eariier, significant differences exist within Hispanic 
subgroups in the United States on both economic and cultural levels. The combination of 
these subgroups into one group may mask important differences in health status. 



27 

The National Medical Expenditure Survey 

The 1987 National Medical Expenditure Survey (NMES) was designed to provide 
estimates of health care use and expenditures as well as information on insurance 
coverage and health status. The survey has been used in a number of studies thus far to 
study health related issues. For example, researchers have examined the characteristics of 
employer-sponsored health insurance (e.g., Seccombe, Clarke, & Coward, 1994; Monheit 
&. Vistness, 1994; Coward. Clarke, & Seccombe, 1993; Cooper & Monheit, 1993), issues 
related to prescription medications (e.g., Hahn, 1995; Willcox, Himmelstein, & 
Woolhandler, 1994; Olfson & Pincus, 1994c), characteristics of nursing homes and 
nursing home residents (see Murtaugh & Freiman, 1995; Romeis, 1994; Short & Kemper, 
1994), and mental health care (i.e., Olfson & Pincus, 1994a, 1994b; Shea, Streit, & 
Smyer, 1994; Smyer, Shea, and Streit, 1994; Shea. Smyer, & Streit, 1993). The database 
has also provided information on access to and utilization of health care (i.e., 
Cunningham & Cornelius, 1995; Himmelstein & Woolhandler. 1995; Hahn, 1994; 
Cornelius, 1991, 1993; McKinney & Marconi, 1992) and health care financing and 
expenditures (see Rasell, Bernstein, & Tang, 1994; Short &, Lair, 1994; Rubin, Altman, 
&Mendelson, 1994). " ^ 

There are a few studies that have used this database to specifically explore issues 
related to subjective health status. For example, Franks et al. (1993) examined the 
relationship between health insurance and subjective health status using the 1987 
National Medical Expenditure Survey. They compared adults with private or military 
insurance to adults without insurance for a year on several measures of subjective health 



28 

status: health perception, mental health, physical functioning, and role functioning. They 
controlled for medical conditions, attitude toward the value of health care and insurance, 
family income, education, and race/ethnicity. Their analysis found individuals without 
health insurance had lower perceived health status compared to individuals with health 
insurance even after adjusting for the above mentioned potential confounding variables. 
They found those without health insurance to be younger, more likely to be male, less 
likely to be white, more likely to have a family income below the poverty level, less 
likely to have graduated from high school, and to have more negative attitudes towards 
the value of health insurance and medical care than those with health insurance. 

Short and Lair (1994) also used the 1987 NMES to compare the health status of 
the insured and uninsured. However, in an attempt to determine potential differential 
expenditure patterns across health insurance subgroups, their analysis further 
distinguished between five different coverage groups: privately insured with 
employment-related insurance; privately insured with nongroup insurance; persons who 
qualified for public insurance on the basis of their poor health; persons who qualified for 
public insurance by virtue of low family income; and the uninsured. Exploratory factor 
analysis of health status questions from the Self-administered Health Status 
Questionnaire of the NMES survey produced 1 1 scales used in the analysis. They found 
significant differences across the five health insurance groups on self reported health 
status. Adults in employer-sponsored plans were the healthiest followed by those with 
nongroup private insurance, the uninsured group, the low-income publicly insured group, 
and lastly those who qualified for Medicare or Medicaid based on poor health. Overall, 
their analysis found that on all health status scales but one (developmental disability). 



29 



individuals with public insurance were less healthy than either the uninsured or the 
privately insured. One possible explanation for the differential health status among the 
uninsured and those with public insurance cited in the authors review is that people who 
are in relatively good health may perceive the costs of purchasing health insurance 
outweigh the possible benefits of having health insurance. Interestingly, unlike the Frank 
et al. (1993) study, the authors did not explore the relationship between being uninsured 
and having negative attitudes towards insurance and health care. 

There has been some limited use of the National Medical Expenditure Survey to 
examine health care issues specific to minority populations. Cornelius (1993) examined 
access to health care for minority children and found the African American and Hispanic 
children were more likely than White American children to be poor, uninsured members 
of single-parent households and to wait longer to see a medical provider. Specifically, 
she found 14% of White American children were uninsured compared to 21.6% of 
African American children and 32.6% of Hispanic children. She also found 
approximately twice as many African American and Hispanic children were reported to 
be in fair or poor health compared to White American children. Moy and Bartman (1995) 
examined the relationship between physician race and the care of minority and medically 
indigent patients. They found that racial/ethnic minorities and medically indigent 
patients were more likely to receive care from nonwhite physicians. Looking at health 
status outcomes, the analysis revealed that these patients also tended to be sicker (were in 
fair or poor health, received an emergency department service, and/or were hospitalized). 
Seccombe, Clarke, and Coward (1994) examined the effects of sociodemographic 
and employment factors on discrepancies in employer-sponsored health insurance among 



30 

minorities. Their analysis found minorities were less likely to have medical insurance 
provided to them by their employers and were more likely to be uninsured compared to 
white Americans. 

A review of the literature using the 1987 National Medical Expenditure Survey 
found only two studies that evaluated the relationship between health behaviors and 
health status. Stoddard and Miller (1995) examined the impact of parental smoking 
behavior on the incidence of wheezing respiratory illness in children. Cornelius (1991) 
looked at the health habits of school-age children. Thus far, this database has not been 
used to examine the relationship between health related behaviors and health status 
among minority populations. 

Policv Implications 

The broad social, economic, and medical circumstances that mediate the 
differential health status of various groups within the United States have serious 
implications for health care reform and related public policy initiatives. The provision of 
universal health insurance and increased access to health care may present an ideal 
avenue to closing the gap in health status across groups. However, given the escalating 
costs of health care and the current political atmosphere, increased funding for public 
insurance programs and initiatives for increasing access to health care services for the 
poor appears unlikely. In addition, while disparities in socioeconomic status across 
groups account for a portion of the difference in health status and mortality rates among 
minorities and white Americans, a review of the literature suggests that it does not 
account for all the variance. Differences in the social or physical environment between 



31 

African Americans and white Americans may be partially responsible. Variations across 
groups in health related behaviors may also play a significant role in overall health status. 
Ultimately, attention will have to focus on health outcomes and interventions will 
need to extend beyond the health care system to broader social, economic, and 
educational systems (Nickens, 1995). Psychology could play a significant role, 
addressing these broader systems through preventative health care and education. 
However, it will be profession's responsibility to successfully educate minority 
populations as to the impact of behavior on health and the importance of preventative 
health care, recognizing the influence of cultural patterns, socioeconomic status, and 
health related behaviors on health status and health outcomes. 

Purpose of Research 

Differences in health related behaviors may help explain some of the variance 
associated with the differential health status of minority populations. The purpose of this 
study is to examine the relationship between race/ethnicity, health related behaviors, and 
self-reported health status. Given the established association between health, 
socioeconomic status, and the probability of having health insurance, the effects of SES 
and insurance coverage will also be examined as they relate to race/ethnicity and health. 

Specific exploratory model of interest 

As illustrated in figure 2. a hierarchical causal model will examine the 
relationship between health related behaviors and self-reported health status. In the 
model, four groups of variables will be included as direct predictors of health status: (1) 
race, (2) socio-demographic indices, (3) health insurance, and (4) health related 



32 ' ^ 

behaviors. Components of socio-demographic dimension include sex, age, education, 
income, and marital status. Self-reported health status will be measured through a 
number of components: Overall Health Rating, Subjective Health Status, Role 
Functioning. Physical Functioning. Acute and Chronic Symptoms, and presence of 
Medical Conditions. 

Several indirect effects on self-reported health status are also proposed in the 
model. The effect of demographic variables on (a) insurance coverage and (b) health 
related behaviors will be examined. A secondary model will explore the effects of race 
and socio-demographic variables on (c) attitudes towards health care/health insurance as 
well as the effects of (c) attitudes towards health care/health insurance on (d) health 
insurance coverage. 

Previous investigations have explored several pathways presented here. For 
example, Seccombe. Clark, and Coward (1994) examined the influence of demographic 
variables on insurance coverage. Short and Lair (1994) have looked at the relationship 
between insurance coverage and health status as well as the health status of the uninsured 
mediated by family income level. Franks et al. (1993) also examined the effects of health 
insurance coverage on subjective health status. Cornelius (1993) investigated barriers to 
health care for white American. African American, and Hispanic children. A subset of 
her analysis observed the direct effects of perceived health status on racial/ethnic group 
membership. Based on comprehensive literature review, no study to date has used this 
database to explore the relationship between health related behaviors among minority 
groups and self-reported health status. 




I 

u 

o 
u 

JS 



a 
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3 
00 



CHAPTER 2 
METHODOLOGY 



Data and Sample 

Data used in this study were obtained from the Household Survey component of 
the 1987 National Medical Expenditure Survey (NMES). The National Medical 
Expenditure Survey (NMES) was sponsored by the Agency for Health Care Policy and 
Research and used a national probability sample of civilian, noninstitutionalized U.S. 
citizens. The survey was designed to provide nationally representative estimates of 
health status, health insurance coverage, use of health care services, health care 
expenditures, and sources of payment for the period from January 1 to December 31, 
1987. 

The NMES used a multistage stratified area probability design of approximately 
15,000 households in the United States representing a total sample of 34,600 persons. To 
provide more accurate analysis of underrepresented populations and in an effort to 
address policy concerns specific to certain populations, the NMES oversampled African 
Americans, Hispanics, the elderly, the functionally impaired and low-income families. 

The Household Survey component of the NMES was conducted over four rounds 
of personal and telephone interviews at 4-month intervals. A short telephone interview 
constituted a final fifth round. Baseline data on household composition and employment 

34 



35 

and insurance characteristics were updated for each quarter, and information on all use of 
and expenditures for health care services and sources of payment was obtained. The 
response rate for the Household Survey across all five rounds of data collection was 
approximately 80% of total households identified. In addifion to personal and telephone 
interviews, a self-administered Health Status Questionnaire for adults and children was 
mailed to participants between the first and second interview rounds and collected 
information on health habits, self-assessed health status, mental health, functional status, 
health attitudes, vision and hearing, and preventive care. The self-administered 
questionnaire included checklists of the most common chronic conditions and a checklist 
of symptoms that asked whether participants had experienced the symptoms in the 
previous 30 days and whether they had seen a physician about them. Separate 
questionnaires were developed for children and adults. 

The present investigation used demographic and health insurance data from the 
second round of the Household Survey interview and information gathered from the self- 
administered Health Status Questionnaire. This study is restricted to data obtained from 
adults, ages 18 and older, who were denoted as heads of household and who responded to 
the Self Administered Household Survey questionnaire portion of the survey, resulting in 
a total sample size of 12272. Adjustments for missing data resulted in final sample size 
of 11287. ., . 



36 

Measures 

Independent Variables 

The independent variables of interest in this study included race/ethnicity, socio- 
demographic variables (gender, age, marital status, education, poverty status), health 
insurance coverage (private, public, uninsured), attitudes towards health care and health 
insurance, and health related behaviors. Six dependent measures were constructed to 
reflect overall health status: Overall Health Rating, Role Functioning, Physical 
Functioning, Chronic Symptoms, Acute Symptoms, and Medical Conditions. 

In the NMES Household Survey, participants were asked to best classify their 
ethnic/racial background as American Indian, Alaskan Native, Asian or Pacific Islander, 
black, white, or other. In addition, participants were asked if their main national origin 
was among the following Hispanic subgroups: Puerto Rican, Cuban, Mexican or 
Mexicano, Mexican American or Chicano. other Latin American, or other Hispanic. 
Using similar data collection instruments and interview procedures over the same time 
period, NMES conducted a separate survey of American Indians and Alaska Natives 
living on or near reservations and eligible for services from the Indian Health Service. 
However, due to differences in questionnaires across the two separate surveys, the results 
from the Survey of American Indians and Alaska Natives were not included in this 
analysis. Sample size for Asian or Pacific Islanders was small and. as such, were not 
included in this analysis. Individuals classified as "other" were also not included in this 
study. In addition, sample sizes for the Hispanic subgroups were small and thus did not 
permh separate analysis. As a result, all Hispanic subgroups were combined into a single 



' ' ' i 



37 

group. Given the heterogeneity of national origin and the cultural differences that may 
exist across subgroups of the Hispanic population, results related to Hispanic outcomes 
should be considered cautiously. Race/ethnic classifications were dummy coded as 
African American (yes=l. no=0), White American (yes=l, no=0), Hispanic (yes=l, 
no=0). 

Participants were classified as having one of the following insurance coverage: 1) 
Uninsured; 2) Public insurance (Medicaid and other public medical assistance); 3) Private 
insurance. Coverage through CHAMPUS (Civilian Health and Medical Program of the 
Uniformed Services) was included under private insurance category since coverage 
benefits more closely approximate private rather than public insurance (Hahn,1994). For 
analytical purposes, the insurance variable was dummy coded as Uninsured (yes=l, 
no=0), Public (yes=l, no=0), and Private (yes=l, no=0). 

Poverty status was used in this investigation as a measure of socioeconomic 
status. As part of the NMES Household survey, income data were collected on 26 
separate sources of incomes. An aggregated income measure was then developed by 
NMES for each person by summing over each of the income sources. Family income 
measures were constructed for each person by summing over the income for each person 
in the person's family and then combining these amounts into the single family measure. 
The person's family income was then compared to the official poverty threshold for 1987 
for the appropriate family size to create a poverty status variable. Poverty status was 
initially categorized by NMES into 6 categories (poor, near poor, low income, middle 
income, high income, negative income). However, due to skewed distribution of income 
data, for this present investigation, the poverty status variable was re-categorized and 



38 

dummy coded into poor income (yes=l, no=0), middle income (yes=l, no=0), high 
income (yes=l, no=0). Participant's age was coded as a continuous variable. Marital 
status was treated as a dichotomous variable. Individuals who reported being single, 
separated, divorced, or widowed were combined into one category and coded as not 
married (married=0). Individuals who reported being married were coded as married 
(married=l ). The distribution of data from the education variable was skewed. 
Therefore, individuals were re-categorized into three groups (less than high school, high 
school, more than high school). 

The self-administered health status questionnaire included ten questions related to 
participants' attitudes towards health insurance and health care in general. Questions 
were rated on a 5 point likert scale, with higher scores indicating more negative attitude 
towards the value of health care and health insurance. For this analysis, two separate 
subscales were developed. The first subscale consisted of four questions measuring 
attitudes towards health insurance and the second subscale consist of six questions 
assessing attitudes about health care in general. Cronbach's alpha calculated for these two 
scales were .43 and .57 respectively. Following the proposed model in Figure 2, 
preliminary regression analysis was conducted to determine degree to which attitude 
towards health care and attitude toward health insurance could be predicted from 
demographic variables as well as degree to which the two attitude scales could predict 
type of insurance coverage. However, results from preliminary analysis found no 
significant amount of variance accounted for. Given the low alpha associated with both 
scales (suggesting poor reliability) as well as nonsignificant relationship with variables of 



39 

interest, both scales were dropped from further analysis, and as a consequence, the 
attitude component of Figure 2 was deleted. 

For this investigation several items from the self-administered health status 
questionnaire were used to measure various health related behaviors: Body mass index, 
smoking index, regular physical exercise, blood pressure checked within past year, eating 
breakfast, and frequency of wearing a seat belt. Two separate indices of weight were 
calculated for this study. Body Mass Index was calculated by dividing self-reported 
weight (kg) by self-reported height (meters) squared. For ease of interpretation in logistic 
analysis, a binary variable of Overweight was developed. Based on cutoffs obtained 
from Health People 2000 (Department of Health and Human Services, 1992), individuals 
with a body mass index equal to or greater than 28 were characterized as being 
overweight (yes=l). Individuals with a body mass index less than 28 were characterized 
as not overweight (no=0). Two separate measures of smoking behavior were also used 
for this study. For logistic regression, a dichotomous Ever Smoked index was developed. 
Individuals who reported having smoked more than 100 cigarettes in lifetime were 
categorized as 1 (yes), otherwise they were categorized as (no). To allow for greater 
specificity in hierarchical regression analysis, an index of total cigarettes smoked in 
lifetime was created. For current smokers, age started smoking was subtracted from 
present age and multiplied by 365 to obtain total number of days smoked. This number 
was then multiplied by number of cigarettes smoked per day to obtain total number of 
cigarettes smoked in lifetime. For previous smokers, the age started smoking was 
subtracted from the age the participant stopped smoking and multiplied by 365 to obtain 
total number of days smoked. This number was similarly multiplied by number of 



40 

cigarettes smoked per day. Individuals who reported never having smoked were included 
as having zero number of cigarettes smoked per day. Variables for whether the 
participant eats breakfast and wears a seat belt were initially measured on a 4 point scale 
(never, seldom/rarely, almost daily, or always/everyday). However, due to skewed 
distribution of scores, both variables were dichotomized into yes (almost daily, everyday) 
and no (never, seldom/rarely). The eating breakfast variable was ultimately excluded for 
further regression analysis due to non-significant effect on all models. Regular physical 
exercise and blood pressure check were both coded as dichotomous variables (yes=l, 
no=0). 
Dependent Variables 

For this investigation, several health related measures were initially developed 
from questionnaire items to serve as dependent variables: Overall Health Rating, 
Subjective Health Scale, Mental Health Status, Role Functioning, Physical Functioning, 
Acute Symptoms, Chronic Symptoms, and Medical Conditions. For each of these scales, 
Cronbach's alpha was calculated to determine reliability of scales in relation to summed 
items. 

To obtain participants" Overall Health Rating, they were asked to rate their health 
as excellent, good, fair, or poor. The Health Rating scale was normally distributed. The 
Subjective Health scale consisted of four separate questions rated on a four point scale 
(a=.87). However, due to non-normal distribution of the scale as well as it's similarity to 
and high correlation (.70) with the Overall Health Rating scale, the Subjective Health 
scale was dropped from further analysis. The Role Functioning scale consisted of two 






41 

questions related to the degree health keeps the individual from performing certain tasks 
(a=.81). The Physical Functioning scale consisted of five questions related to the degree 
health limits various physical activities (a=.87). " '' : 

The Acute Health Symptoms scale originally consisted of nine questions related 
to whether the participant demonstrated particular symptoms (i.e., sudden feeling of 
weakness, repeated indigestion or upset stomach) within the past thirty days. Three 
symptoms (high fever, skin rash, and bleeding) were removed from the scale due to low 
correlation with other variables in scale; the resulting scale had an alpha of .65. The 
chronic health symptoms scale included 5 questions related to more chronic symptoms 
(i.e., repeated backaches, frequent headaches). Similarly, due to low correlation with 
other variables in scale, one variable (hemorrhoids) was dropped from the scale. The 
resulting scale had an alpha of .52 . The Household Survey Questionnaire also included a 
checklist of 1 1 serious medical conditions which comprised the medical conditions scale 
(a-.69). ; 

The Acute Health symptoms. Chronic Health symptoms, and Medical Conditions 
scales all demonstrated skewed distribution, with individuals acknowledging either no 
symptoms or one or more symptoms. Consequently, dummy variables were created 
(either having any one of the symptoms or condition or not) for further analysis by 
logistic regression. 
Missing Data ;-'•._ _ , 

Certain responses to questionnaire items were treated as missing data. These 
include the responses categories: 1 ) did not ascertain, 2) don't know, 3) refiised, and 4) 



t(K':-V » 



- V 



42 

inapplicable. Full regression models using the SAS system for computer statistical 
analysis employed listwise deletion of missing data resulting in a sample size of 1 1287. 
In order to facilitate comparison across models, the reduced sample size of 1 1287 was 
used for all analysis in this study. Elimination of missing data reduced overall sample 
size by 8%. 



Cr V, : 






CHAPTER 3 
RESULTS 



Data Analysis. 

The purpose of this study was to examine the relationship between race/ethnicity 
and health status while controlling for various socio-demographic variables, health 
related behaviors, and type of insurance coverage. Data were first analyzed to determine 
if differences existed across racial/ethnic groups on independent and dependent variables. 
Chi-square and F tests were computed to determine whether differences were statistically 
significant. Correlations were then estimated within sets of independent variables (i.e., 
socio-demographic, health related behaviors) and among dependent variables to explore 
degree of association among measures (Statistical software automatically produces point- 
biserial and phi-coefficient for dichotomous data; Cohen & Cohen, 1983)). 

Multiple regression analysis was used to determine whether the effects of 
race/ethnicity persist with the stepwise inclusion of sets of variables proposed to mediate 
the relationship between race/ethnicity and health status (stepwise inclusion followed 
direction proposed in the model depicted in Figure 2). Hierarchical multiple regression 
was used for analysis of continuous dependent variables while hierarchical logistic 
regression was used for dichotomous dependent variables. • . 



43 



44 

Supplemental regression analysis was used to analyze the direct relationship 
between race/ethnicity and the variables hypothesized to mediate of the effect of 
race/ethnicity on health status. First, the relationship between health insurance coverage 
and race was analyzed, controlling for socio-demographic variables (pathway a on Figure 
2). Second, the relationship between race and health related behaviors was analyzed, 
controlling for the effects of socio-demographic variables (pathway b on Figure 2). 
Descriptive Analvsis 

Tables of descriptive analysis are presented at the end of the section. Table 3 
presents racial/ethnic comparisons among both dependent and independent variables of 
interest. There was a significant difference in the gender composition of the sample with 
women comprising a majority (55%) of the Black sample but a minority of the White 
(36%) and Hispanic sample (36%). Whites in the sample were relatively older with a 
mean age of 51 compared to Blacks (mean age=47) and Hispanics (mean age=43). 
Sample differences in income level also emerged with 15% of Whites being categorized 
as poor compared to 26% of Hispanics and 34% of Blacks. Blacks were significantly less 
likely to be married (35%) compared to both Whites (56%) and Hispanics (57%). In 
terms of the educational distribution of the sample, approximately 50% of the Hispanics 
had not completed high school compared to 42% of Blacks and 29% of Whites. In 
addition, more Whites had post-secondary education (38%) compared to Blacks (25%) or 
Hispanics (23%). There were significant differences in insurance coverage across the 
three racial/ethnic groups. Of the Whites in the sample, 80% were privately insured 
compared to 57% of Blacks and 56% of Hispanics. In addition, approximately 25% of 
Hispanics were uninsured compared to 10% of Whites and 15% of Blacks. 



45 

There were significant differences across racial/ethnic groups on health related 
behaviors. Of the Whites in the sample, 54% reported engaging in regular physical 
activity compared to 40% of Blacks and 44% of Hispanics. Black had the highest mean 
body mass index and a greater proportion of overweight persons (30%) compared to 
Whites (19%) or Hispanics (23%). Of the three groups in the sample. Blacks were the 
least likely to regularly wear seat-belts while Hispanics were the least likely to report 
having had their blood pressure checked within the past year. More Whites reported 
having ever smoked (defined as smoking over 1 00 cigarettes in lifetime) compared to 
Blacks or Hispanics. In addition. Whites had a significantly higher number of cigarettes 
smoked in lifetime than to Blacks and Hispanics. 

Table 4 presents percentage distributions and tests of significance for health 
outcome variables. On the Overall Health Rating scale. Whites and Hispanics had similar 
ratings of excellent and good health, while Blacks had lower ratings of excellent and 
good health. On the Role Functioning scale, significantly more Blacks reported not being 
able to work due to health problems (18%) compared to Whites (14%) and Hispanics 
(11%). Approximately 23% of both Blacks and Whites reported that their health limits 
the kinds of work they can do compared to only 14% of Hispanics. Significant 
differences also occurred across items on the Physical Funcfioning scale. Interestingly, 
while more Blacks indicated that their health limits moderate activity (22%) compared to 
Whites (18%), more Whites reported that their health limits vigorous activity (42%) 
compared to Blacks (37%). Hispanics reported the least limitations in moderate (13%) or 
vigorous (26%) activity. Finally, more Blacks reported having trouble walking a block, 
climbing stairs and lifting, bending, or stooping compared to Whites and Hispanics. 



46 

Differences by race/ethnicity on the health outcome scales related primarily to 
medical conditions are presented in Table 5. On the Acute Symptoms scales, 
significantly fewer Hispanics (42%) endorsed symptoms than either Whites (50%) or 
Blacks (50%). A similar pattern of results emerged for both the Chronic Symptoms 
scale and the Medical Conditions scale. 

Table 6 presents correlation coefficients among the socio-demographic variables 
of race, gender, age, marital status, education, income, and insurance coverage. Results 
found significant relationships between race and socio-demographic variables. For 
example, comparisons between Whites and Blacks found Whites were more likely to be 
older, have higher incomes, higher educational levels, to be married, and to be privately 
insured. A similar pattern of association occurred between Whites and Hispanics, 
however, there was no correlation for marital status. The significant differences across 
racial/ethnic groups that emerged on the various socio-demographic dimensions included 
in this study suggest that these dimensions may influence the relationship between 
race/ethnicity and health status and lend credence to their inclusion in multivariate 
analysis as mediating variables. 

Males in the sample were significantly more likely than females to be married, 
have higher income levels, and be either privately insured (compared to publicly) or 
uninsured (compared to publicly). As one would expect, higher educational levels were 
significantly associated with higher income levels. In addition, higher income levels was 
correlated with possession of private insurance (compared to public or being uninsured). 

Table 7 presents correlation coefficients for the health related behaviors. There 
was a positive correlation between engaging in physical activity and wearing seat-belts, 



47 

indicating that those who engage in regular physical activity also tend to regularly wear 
their seat-belts while driving. Physical activity was negatively correlated with body mass 
index as well as smoking index, indicating that individuals who engage in regular 
physical activity tend to weigh less and smoke less than individuals who do not engage in 
regular exercise. There was a positive correlation between body mass index and having 
had blood pressure checked; individuals who weigh more were also more likely to have 
had their blood pressure checked within the past year. 

The correlation coefficients for the health outcome variable scales are presented in 
Table 8. Overall, the outcome scales were moderately correlated with each other, 
suggesting that they are most likely tapping into a similar construct: health status. Self- 
report measures of functioning (Overall Health Rating, Physical Functioning, Role 
functioning) tended to have somewhat higher correlations than the scales that measure 
actual symptoms (Acute Symptoms, Chronic Symptoms, Medical Conditions). However, 
the fact that all scales, expect for the association between Physical Functioning and Role 
Functioning, are not very highly correlated provides some evidence that they are also 
measuring unique components of that construct. The exception was Role Functioning 
and Physical Functioning, which were highly correlated (r^-.79). Nevertheless, they 
were treated as separate outcome variables in this analysis to allow for greater specificity 
(i.e., differences in amount of variance accounted for in multivariate analysis across the 
two scales may provide important information about degree which there are racial/ethnic 
differences in the way health limits the ability to perfonm a role (i.e., work) verses 
limiting daily physical functioning (i.e., housework). 



48 



Tables. Percentage Distribution and Test of Significance of Demographic 
Variables. Insurance Status, and Health Related Behaviors by Race/Ethnicity 



Variable 



Demographic Variables 



White 



Race/Ethnicity 



Black 



Hispanic 



F or 
Chi-square 



Gender 

%Maie 
%Female 

Age (Mean) 

(SD) 
Poverty Status 

%Poor 

%Low-Mid 

%High 

% Married 

Education 

%<High school 
%High school 
%>High school 

Insurance Status 

%Private 

%Public 

%Uninsured 

Health Related Behaviors 

BodyMass Index(Mean) 

(SD) 
%Overweight 
%Physical Exercise^ 
%Ever Smoked^ 
#ofCig/Life(Mean) 
(SD) 
%Wear Seat-belt^ 
%Blood Pressure Check^ 



64.1 


44.9 


63.6 


35.9 


55.0 


36.3 


51.1 


47.4 


43.5 


(19.1) 


(17.2) 


(15.C 


15.3 


33.8 


26.4 


48.5 


46.8 


, 52.1 


36.2 


19.4 


21.5 



56.0 



35.0 



57.6 



290.9* 



113.1' 



553. r 



344.7" 



28.9 


41.5 


49.7 


349.6* 


34.8 


32.2 


25.5 


36.8* 


37.6 ' 


24.9 


22.8 


192.9* 


80.4 


57.4 


56.2 


820.7* 


9.2 


27.2 


18.5 




10.4 


15.4 


- 25.3 




24.8 


26.3 


25.6 


83.3* 


(4.8) 


(5.6) 


(4.8) 




18.7 


29.1 


23.2 


126.0* 


53.9 - 


40.3 


44.9 


153.8* 


61.6 


55.1 


50.6 


63.4* 


105243 


52969 


44424 


152.4* 


(169880) 


(111911) 


(96432) 




53.7 


45.6 ^, 


53.1 


46.5* 


76.4 


75.62 


63.9 


73.2* 



a%yes, * p<0.0001 

Overweight = Bodymass index >= 28, Ever Smoked = Smoked over 100 cigarettes in lifetime 



49 



Table 4. Percentage Distribution and Test of Significance of Outcome Variables bv 



Race/Ethnicitv 






Race/Ethnicity 








For 


Variable 


White 


Black Hispanic Chi-square 


Rate Health 




' 


100.7* 


Mean (SD) 


2.07(.81) 


2.25(.81) 


2.08(.79) 


%Excellent 


24.1 


16.3 


23.0 


%Good 


49.8 


49.3 


50.3 


%Fair 


20.5 


27.6 


21.8 


%Poor 


5.7 


6.8 


4.9 



Role Functioning 

Mean (SD) 
%Can't work due to health 
%Health limits kind of work 



57.9" 



.37(.71) 


.41(.75) 


.25(.60) 




14.0 


17.7 


10.9 


31.0* 


23.2 


23.6 


13.5 


49.1* 



Physical Functioning 

Mean (SD) 
%Health limits mod. Activity 
%Health limits vig. Activity 
%Trouble walking one block 
%Trouble climbing stairs 
%Trouble lift/bend/stoop 



%yes 

* p<0.0001 



Means on scales: Higher scores = worse functioning 



156.8^ 



1.16(1.64) 


1.25(1.81) 


.76(1.44) 




f 18.4 


21.5 


13.3 


31.2* 


42.4 


36.6 


25.9 


113.0* 


11.5 


16.7 


7.7 


65.9* 


20.7 


25.2 


14.7 


49.4* 


23.2 


25.2 


14.7 


45.1* 



50 



Table 5. Percentage Distribution and Test of Significance of Medical Conditions 
Scales by Race/Ethnicit\' 



Race/Ethnicity 



Variable (% yes) 



White Black Hispanic Chi-square 



Acute Symptoms Scale 49.6 49.6 



41.9 



21.4" 



Chronic Symptoms Scale 50.9 49.4 



Medical Conditions Scale 45.5 



46.9 



40.5 



30.4 



32.4" 



87.6-' 



* P<0.001 

Scales are coded (1=1 or more symptoms, 0=no symptoms) 



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54 



Multivariate Analysis 



Following recommendations by Cohen and Cohen (1983), multiple and logistic 
regression analysis was used to test the causal model depicted in Figure 2. Table 21 
provides a list of all variables included in the regression analysis along with 
corresponding coding. For the dependent variables of Overall Health Rating, Role 
Functioning, and Physical Functioning, hierarchical multiple regression was used to test 
the predictive model. For all three dependent variables, race/ethnicity was entered in the 
first step of the model. As mentioned previously. White, Black, and Hispanic were 
dummy coded as either having the attribute or not (White (l=yes,0-no), Black (1-yes, 
0=no), Hispanic (l=yes. 0=no)). Black and Hispanic were included in the model with 
White left out, thereby allowing comparison of both Blacks and Hispanics to White. The 
second step added socio-demographic variables to the model. The three categories of 
income were dummy coded into poor(l=yes, 0=no), middle income (l=yes, 0=no), high 
income (l=yes, 0-no)) and both poor and middle income were included in the model, 
allowing comparison to high income. Gender was coded as male (l=yes, 0=no), allowing 
comparison to females. Marital status was coded as married (l=yes) (0=no), comparing 
to those not married. Education was dummy coded into less than high school (l=yes, 
0=no), high school (l=yes, 0=no), and more than high school (l=yes, 0=no). Less than 
high school and high school were included in the model, allowing comparison to 
individuals with more than a high school education. Finally, age was treated as a 
continuous variable. In the third step, health related behaviors were added to the model. 



55 

Body Mass Index and Smoking Index were treated as continuous variables while Physical 
Exercise, Blood Pressure Check, and Wear Seat Belt were coded as l=yes and 0=no. For 
the final ftill model, health insurance coverage was added to the sets of independent 
variables. Public ( 1 =yes,0=no), private (l=yes, 0=no), and uninsured (l=yes, 0-no) were 
dummy coded and both public and uninsured were included in the model, thereby 
allowing comparison to private. 

The dependent variables of Acute Symptoms, Chronic Symptoms, and Medical 
Conditions consisted of binary response data (scales coded as either no symptoms (0) or 1 
or more symptoms (1)). As such, hierarchical logistic regression was used to determine 
the influence of race/ethnicity, socio-demographic variables, health related behaviors, and 
insurance coverage on the odds of having one or more medical symptom (versus having 
no symptoms). The steps for inclusion in the models are identical to the multivariate 
regressions. However, the dichotomous variables of Overweight (yes=l, no=0), and Ever 
Smoked (yes=l, no=0) were used in place of Body Mass Index and Smoking Index for 
ease of interpretation of odds ratios. - 

Separate computational analysis were done for both multiple regressions and 
logistic regressions to allow for across group comparisons of variables within each step of 
model. Specific group comparisons made within steps included: Black vs. Hispanic, high 
school vs. less than high school, poor vs. Middle income, and public insurance vs. 
uninsured. The equation used to obtain appropriate t-values was 

t = Bi-B, 

Vvar pi+ var p2 - 2(covpiP2) 



56 



Overall Health Rating 

Table 9 presents a summary of the regression analysis for variables predicting 
Overall Health Rating. For the first step in the model, race/ethnicity was entered 
independently to estimate the degree to which perceived health status can be predicted by 
race/ethnicity. Race/ethnicity was significant with Blacks being more likely to report 
poorer health status compared to Whites. Separate analysis indicated that Blacks were 
also more likely to report poorer health status than Hispanics (t=5.33). There was no 
difference in perceived health status between Hispanics and Whites. 

Adding the set of socio-demographic variables (gender, age, marital status, 
education, and income level) to race/ethnicity as predictors in the second model 
accounted for an additional 21% of the variance. Age (P=.32), education (<high 
school:P=.19, high school:P=.16), and income level (poor:P=.24, middle:P=14) had the 
largest effect on the model. Separate analysis also found individuals with a high school 
degree and individuals with middle income levels to have significantly better Health 
Ratings then individuals without a degree (t=10.59) or with low incomes (t=12.22). 
Although still significant, there was a 35% reduction in the effect of being Black (i.e., the 
coefficient changed from . 1 7 in model 1 to . 1 1 in model 2) when compared to White, 
indicating that differences in socio-demographic dimensions may help account for some 
of the initial differences found between Blacks and Whites. Overall, individuals with 
poorer ratings of health status were more likely to be Black or Hispanic, female, older in 



■ -MV , 57 

age, have lower educational levels, and to be poor or have middle income levels 
compared to high income. 

In the third model, the set of health related behaviors (Body mass index, smoking 
index, physical activity, blood pressure check within past year, wearing seat belt) were 
added to race/ethnicity and socio-demographic variables as predictors. Health related 
behaviors accounted for an additional 6% of the variance. The inclusion of health related 
behaviors to the model accounted for an additional reduction in the effect of being Black, 
although it remained statistically significant. Inclusion also eliminated the effects of 
being male. The reduction of effect suggest that differences in health behaviors may 
mediate some of the initial differences found based on race, age, and gender. In the third 
equation, poorer ratings of health are associated with being Black, older, having lower 
educational levels, low to middle income levels, weighing more, smoking more, not 
engaging in regular physical activity, having had blood pressure checked within past year, 
and not using seat belts regularly. 

In the final model, insurance coverage was added to the sets of predictor variables, 
explaining an additional 1% of the variance in the model. Compared to model 2, adding 
insurance further reduced the Black effect by 25% and poor effect by 23%, although they 
remained significant in the model. Uninsured individuals had poorer health ratings 
compared to the privately insured (B=.09). Publicly insured individuals had the worst 
health ratings when compared to the privately insured (B=.26) or uninsured (AB=.26-.09, 
t=6.54). 

For the full regression model, race/ethnicity, socio-demographic variables, health 
related behaviors, and type of insurance coverage explained a total of 28% of the variance 



58 

associated with perceived health status. Compared to the initial model with race alone, 
the fiall model resulted in a 61% reduction in the Black effect. Overall, poor health rating 
was associated with being Black, older, poor or middle income, weighing more, smoking 
more, not engaging in physical activity, having had blood pressure checked within past 
year, not wearing seat belts regularly, and being uninsured or publicly insured. There 
were no effects for Hispanics in the model indicating that Hispanics rate their overall 
health in a similar manner as Whites. 
Role Functioning 

Table 10 presents results of regression analysis for Role Functioning. 
Race/ethnicity accounted for a small portion of the variance with Hispanics reporting 
significantly better role functioning than both Whites (B=-13) or Blacks (AB=-.13-.04, 
t=-6.15). Blacks reported significantly poorer role functioning than Whites (B=.04). The 
addition of socio-demographic variables to the model increased the amount of variance 
accounted for to 20%. The inclusion of demographic variables eliminated the initial race 
effect for Blacks and reduced the effect for Hispanics by 38%, suggesting the influence of 
these demographic variables in accounting for some of the variance previously associated 
with race. Similar to the Overall Health Rating scale, both age (p-.36) and income level 
(poor: P=.21, middle:p=.08) accounted for a large portion of the socio-demographic 
effect. Separate analysis indicated that low income individuals were more likely to report 
poorer role functioning than those having a middle income level (AB=.38-.l 1, t=16.87). 
Poorer role functioning was significantly associated with being older, not married, having 
less than a high school education, and low to middle income levels. 



59 

In the third model, the inclusion of health related behaviors accounted for an 
additional 4% of variance. Addition of health related behaviors slightly reduced the 
effect of age (P=.30 vs. p=.36 in model 2), although age remained significant. 
Interestingly, inclusion of the health related behaviors increased the effect of being Black. 
This finding suggests that once certain health related behaviors are controlled for. Blacks 
actually report beller, although not significantly, role functioning, compared to Whites. 
Increased Body mass and smoking, lack of physical activity, and having had blood 
pressure checked within past year were all associated with poorer role functioning. 

The final model included the insurance variable and accounted for an additional 
2% of the variance. Both being uninsured and publicly insured were associated with 
significantly poorer role functioning compared to the privately insured. In separate 
analysis, publicly insured individuals were found to report significantly poorer role 
fimctioning compared to the uninsured (t=l 3.04). The inverse Black effect that emerged 
in model 3 becomes significant with the inclusion of insurance coverage. In addition, the 
significant effect for Hispanics increases further. Although still significant, the effect of 
being poor is reduced by 27% with the addition of the insurance variable to the model. 
The effect of body mass was eliminated. Age, lack of regular physical activity, and 
possessing public insurance had the largest effects on the model. 

The full regression model measuring the impact of race, socio-demographic 
variables, health related behaviors, and insurance coverage on individuals perceived role 
functioning accounted for a total of 27% of the variance. Overall, poorer role fimctioning 
is associated with being male, older in age, having lower income levels, more lifetime 



60 

smoking, not engaging in physical activity, having had blood pressure checked within 
past year, being uninsured or possessing public insurance, and being White. 
Physical Functioning 

Results for the dependent variable of physical functioning are presented in Table 
1 1 and are similar to previous findings for Role Functioning. For the first model of race 
only, Hispanics demonstrated significantly better physical functioning than both Whites 
(B=-.40) and Blacks (AB=-.40-.09, t=-8.16). Blacks were not significantly different 
from Whites in perceived physical functioning. The inclusion of demographic variables 
in the second equation accounted for an additional 31% of the variance and reduced the 
race effects for Hispanics by 43%. Significant effects emerged for gender (male:p=-.04), 
age (P=.46), education (<high school:P=.08), and income level (poor:P=.19, 
middle:P=.08). Overall, being female, older, not married, with less education, and poor 
was associated with poorer physical functioning; being Hispanic was associated with 
better physical functioning while there was no difference between Blacks and Whites. 

Health related behaviors were added to the third model and accounted for an 
additional 4% of the variance. Inclusion of the health related behaviors to the model 
increased the effect of being Black, with Blacks reporting significantly better physical 
fiinctioning compared to Whites. This finding indicates that once variations in health 
related characteristics such as physical activity, smoking, and body mass are controlled 
for, Blacks report less limitations physical functioning than Whites. 

Inclusion of health related behaviors slightly increased the effect of not being 
married but reduced the effects of all the other demographic variables, although all 






'■v 



(except having a high school education) remained statistically significant in the model. 
Higher body mass, more lifetime smoking, not engaging in regular physical activity, and 
having blood pressure checked within past year were associated with poorer physical 
functioning. Lack of regular physical activity had the largest effect (P=-.19) of all the 
health related behaviors included in the model. 

For the final full model, insurance was added and explained another 2% of the 
variance. As with the Health Rating scale and the Role Functioning scale, publicly 
insured individuals reported significantly poorer physical functioning compared to 
privately insured individuals (P=.14). Publicly insured individuals' physical fxmcfioning 
was also significantly poorer than uninsured individuals (AB=.71-.09, t=12.2). There 
was no difference between being privately insured and being uninsured for the Physical 
Functioning scale. Although still significant, the effect of being poor and having middle 
income is further reduced (26% and 12%, respectively) in the full model, suggesting that 
insurance coverage may help explain the income effect. As with Role Functioning, 
inclusion of the insurance variable in the model increased the race effects with both 
Blacks and Hispanics having significantly heller physical functioning compared to 
whites. Overall, individuals with poorer physical functioning are more likely to be white, 
female, older, not married, low to middle income levels, and to weigh more and smoked 
more cigarettes in lifetime, have had their blood pressure checked within past year, and to 
be publicly insured. 



62 

Acute Symptoms Scale 

Results for the logistic regression models predicting Acute symptoms are 
presented in Table 12. For the first model of race/ethnicity alone. Blacks were not 
different from whites in their report of acute symptoms. On the other hand, Hispanics 
were 27% less likely to report acute symptoms compared to Whites. Addition of socio- 
demographic variables to the second model increased the effect for Blacks. Thus, 
controlling for socio-demographic variables resulted in Blacks reporting significantly less 
acute symptoms than Whites. Interestingly, poor individuals were 67% more likely and 
middle income individuals were 25% more likely to report acute symptoms compared to 
high income individuals. Overall, being female, older, poor or middle income, and 
having less education were associated with an increased likelihood of reporting one or 
more acute symptom. Addition of health related behaviors to the model eliminated the 
effect for education and reduced the effect of all other socio-demographic variables 
except for the Black effect. The effect for Blacks increased by 38% from the previous 
model suggesting that when racial differences in health related behaviors and 
characteristics are controlled for. Blacks actually have a significantly lower experience of 
acute symptoms than Whites. Of the health related behaviors, being overweight, having a 
history of smoking (verse no smoking history), not engaging in regular physical activity, 
having blood pressure checked within past year, and not wearing car seat belts were 
associated with an increased likelihood of reporting one or more acute symptoms. 
Addition of health insurance to the final model further increased the Black effect; Blacks 
reported 20% fewer symptoms than Whites while Hispanics reported 24% fewer 



:';/■>• ^ yr,, 






63 

symptoms. Compared to being privately insured, possession of public health insurance 
increased the odds of reporting acute symptoms by 44%. Individuals possessing public 
insurance were also significantly more likely to report acute symptoms compared to 
uninsured individuals (AB=.36-. 14, t=2.72). Although the effect of income was reduced 
with the addition of health insurance, being poor was still significantly associated with 
increased odds of reporting acute symptom (odds ratio: 1.39). 
Chronic Symptoms Scale . ■- 

On the Chronic Symptoms scale, Hispanics were 31% less likely to report 
experiencing any chronic symptoms than Whites. Hispanics were also significantly less 
likely to report chronic symptoms than Blacks (AB=-.36-.06, t=-5.38). There were no 
initial differences between Whites and Blacks. As with the Acute Symptoms scale, 
addition of socio-demographic variables to the model significantly increased the effect of 
being Black (P=-.10) with Blacks being less likely to report chronic symptoms compared 
to Whites. Males were 42% less likely to report chronic symptoms than females. In 
addition, there was a significant effect for education as well as income. Individuals with 
less than a high school education were 26% more likely to report chronic symptoms 
compared those with more than a high school education and poor individuals were 59% 
more likely to report chronic symptoms compared to high income. Poor individuals were 
also significantly more likely to report chronic symptoms compared to middle income 

individuals (AB=.46-.17, t=5.58). Interestingly, individuals who were married were 23% 

■ , s ^ 

more likely to report chronic symptoms. 



64 

Adding health related behaviors to the third model increased the Black effect to 
significance but slightly reduced the effect of being Hispanic compared to White. The 
effect of age was eliminated from the model. With health related behaviors included in 
the model, males were 39% less likely to report chronic symptoms while married 
individuals were 17% more likely to report chronic symptoms. Having a history of 
smoking, not engaging in regular physical activity, having blood pressure checked within 
past year, and not wearing seat belts were all associated with increased odds of reporting 
chronic symptoms. 

Similar to the Acute Symptoms scale, addition of insurance coverage to the model 
further increased the Black effect. Thus, while initially similar in their report of chronic 
symptoms, when the effects of socio-demographics, health related behaviors, and 
insurance coverage are controlled for, Blacks report significantly less chronic symptoms 
than Whites. Being publicly insured increased the odds of reporting chronic symptoms 
by 27%. There was no difference between being privately insured and being uninsured in 
the reporting of chronic symptoms. However, publicly insured individuals were more 
likely to report chronic symptoms compared to the uninsured (AB=.24-.03, t=2.66). 
Medical Conditions Scale 

Similar to the Acute Symptoms and Chronic Symptoms scale, compared to 
Whites, Hispanics were significantly less likely to report experiencing any medical 
conditions (odds:.52). Blacks were not different from Whites in their reporting of 
medical conditions but were significantly more likely to report medical conditions 
compared to Hispanics ( AB=.06-(-.64), t=8.75). Adding socio-demographic variables to 



65 

the model increased the odds of reporting medical conditions to significance for Blacks 
(odds ratio: 1.06 in step 1 to 1.28 in step 2, compared to Whites). In addition, while 
Hispanics were 48% less likely to report medical conditions in step 1, addition of socio- 
demographics reduced that effect by 30%. Older age was significantly associated with a 
higher likelihood of reporting medical conditions. Income had a very strong effect on the 
model; Poor individuals were approximately 70% more likely to report experiencing one 
or more medical condition compared to individuals with high incomes. Poor individual 
were also significantly more likely to report medical conditions than middle income 
individuals (AB=.52-.07, t=7.50). There was no difference between individuals with 
high and middle incomes. 

For the third step in the model, health related behaviors were added and resulted 
in the elimination of the Black effect and a reduction in the Hispanic effect as well as the 
age and poor income effect, although these remained significant. Among the health 
related variables, being overweight increased the odds of reporting medical conditions by 
147%! Having had blood pressure checked within past year was also significantly 
associated with probability of reporting medical conditions (odds ratio:3.05). Having a 
smoking history and not engaging in regular physical activity also significantly increased 
the odds of reporting one or more medical conditions. The final step added insurance to 
the model predicting medical conditions. While there was no difference between being 
privately insured and being uninsured, possessing public insurance was associated with a 
35% increase in the odds of reported medical conditions compared to being privately 
insured. Individuals possessing public insurance were also more likely to report medical 
conditions compared to uninsured individuals (AB=.30-.02, t=2.98). 



66 



Table 9. Summary of Hierarchical Regression Analysis for Variables 
Predicting Qyerall Health Rating 



Variables 



B 



SEB 



rvalue F Value R2 



Step 1 



Step 2 



Step 3 



Step 4 



Intercept 

Black 

Hispanic 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<Higli school 

High school 

Poor 

Middle Income 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 

Body Mass Index 

Smoking Index 

Physical Activity 

Check Blood Pressure 

Wear Seat Belt 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 

Body Mass Index 

Smoking Index 

Physical Activity 

Check Blood Pressure 

Wear Seat Belt 

Uninsured 

Public Insurance 



42.7*** .007 



2.07 


.01 


.00 


241.2*** 


.17 


.02 


.08 


9.29*** 


.01 


.03 


.00 


.44 

398.2*** .2 


1.09 


.03 


.00 


42.3*** 


.11 


.02 


.06 


6.63*** 


.02 


.02 


.01 


.65 


-.07 


.02 


-.04 


-4.19*** 


.01 


.00 


.32 


38.00*** 


.03 


.02 


.02 


2.27 


.32 


.02 


.19 


18.06*** 


.16 


.02 


.09 


9.91*** 


.39 


.02 


.19 


18.28*** 


.17 


.02 


.11 


11.04*** 

312.55*** 


1. 10 


.04 


.00 


24,72*** 


.08 


.02 


.04 


4.81*** 


.02 


.03 


.01 


.81 


-.02 


.02 


-.01 


-1.27 


.01 


.00 


.25 


27.98*** 


.01 


.02 


.00 


.71 


.26 


.02 


.15 


14.09*** 


.14 


.02 


.08 


8.43*** 


.34 


.02 


.17 


16.23*** 


.16 


.02 


.09 


10.05*** 


.00 


.00 


.05 


6.29*** 


.00 


.00 


.08 


9.73*** 


-.34 


.01 


-.20 


-24.49*** 


.17 


.02 


.09 


10.59*** 


-.07 


.01 


-.04 


-5.14*** 

285.56*** 


1.08 


.04 


.00 


24.3*** 


.06 


.02 


.03 


3.41** 


-.00 


.03 


-.00 


-.09 


-.01 


.02 


-.01 


-.82 


.01 


.00 


.25 


27.60*** 


.03 


.02 


.01 


1.64 


.23 


.02 


.13 


12.63*** 


.13 


.02 


.08 


8.19*** 


.26 


.02 


.13 


II.5I*** 


.14 


.02 


.09 


8.97*** 


.01 


.00 


.05 


5.87*** 


.00 


.00 


.08 


9.36*** 


-.33 


.01 


-.20 


-23.80*** 


.17 


.02 


.09 


10.74*** 


-.06 


.01 


-.04 


-4.64*** 


.09 


.02 


.04 


4.31*** 


.26 


.02 


.12 


11.73*** 



.28 



.29 



*p<.01, **p<.001.***p<.000I, R2=Adjusted R-Square 

Race, Education, Income, and Insurance coverage are dummy coded (l=variable, 0=no variable). Gender(Male=l, 
Female=0). Age, Body Mass Index, Smoking Index are treated as continuous variables. Physical Exercise, Check 
Blood Pressure, and Wear Seat belt are codes as l=yes, 0=no. See Table 21 for addition details on variable codes. 





67 




1 


1 

Table 10. Summary of Hierarchical Reeression Analysis for Variables i 


Predicting 


Role Functioning 


1 

i 


Variables B 


SEB 


P 


rvalue F Value R2 1 


Step 1 






19.8*** .003 


Intercept .37 


.01 


.00 


49.72*** 


Black .04 


.02 


.02 


2.47* 


Hispatiic -.13 


.02 


-.05 


-5.37*** 


Step 2 






362.9*** .21 


Intercept -.46 


.02 


.00 


-20.34*** ! 


Black .00 


.02 


.00 


.16 


Hispanic ■ , -.08 


.02 


-.03 


-3.94*** 


Male -.01 


.01 


-.01 


-.99 


Age .01 


.00 


.36 


42.41*** 


Married -.04 


.01 


-.03 


-2.69* 


<High school -.11 


.02 


.07 


7.05*** , 


High school .03 


.01 


.02 


1.84 


Poor .38 


.02 


.21 


20.29*** ; 


Middle Income .11 


.01 


.08 


8.23*** 1 


Step 3 






267.5*** .25 I 


Intercept -.45 


.04 


.00 


-11.45*** 


Black -.02 


.02 


-.02 


-1.76 ; 


Hispanic -.07 


.02 


-.02 


-2.91* J 


Male .03 


.01 


.02 


1.98 i 


Age .01 


.00 


.30 


35.75*** 1 


Married -.05 


.01 


-.04 


-3.67** 


<High school .09 


.02 


.06 


5.73*** 


High school .02 


.01 


.02 


1.71 ; 


Poor .36 


.02 


.20 


18.98*** 


Middle Income .11 


.01 


.07 


7.67*** 


Body Mass Index .00 


.00 


.02 


2.72* 


Smoking Index .00 


.00 


.05 


5.72*** 


Physical Activity -.24 


.01 


-.17 


-19.49*** 


Check Blood Pressure . 1 8 


.01 


.10 


12.36*** 


Wear Seat Belt -.00 


.01 


-.00 


-.35 


Step 4 






360.9*** .27 


Intercept -.45 


.04 


.00 


-11.37*** 


Black -.06 


.02 


-.03 


-3.92*** 


Hispanic -.09 


.02 


-.03 


-4.06*** 


Male .04 


.01 


.03 


2.86* 


Age .01 


.00 


.29 


31.67*** 


Married -.03 


.01 


-.02 


-2.43* 


<High school .06 


.02 


.04 


3.82*** 


High school .02 


.01 


.01 


1.43 


Poor .26 


.02 


.14 


13.10*** 


Middle Income .09 


.01 


.06 


6.56*** 


Body Mass Index .00 


.00 


.02 


1.98 


Smoking Index .00 


.00 


.04 


5.23*** 


Physical Activity -.22 


.01 


-.16 


-18.41*** 


Check Blood Pressure .17 


.01 


.10 


12.27*** 


Wear Seat Belt .00 


.01 


.00 


.29 


Uninsured .04 


.02 


.02 


2.37* 


Public Insurance .35 


.02 


.17 


17.84*** 


*p<.01. **p<.001,***p<.0001. R2=Adjusted R-Square 








Race, Education, Income, and Insurance coverage are dummy coded ( 1 =variable. 


0=no variable). Male (ves=l, no=0>. 


Married (yes=l, no=0) Age. Body Mass Index, Smoking Index are treated as continuous variables. Phvsical Exercise 1 


Check Blood Pressure, and Wear Seat belt are coded as 


=yes, 0=no 


See Table 2 1 for addition details on variable 1 


codes. 









68 



Table 11. Summary of Hierarchical Regression Analysis for Variables 
Predicting Physical Functioning 



Variables 



SEB 



P 



T Value F Value R2 



Step 1 



Step 2 



31.2* 



.004 



Step 3 



Step 4 



627.45*** .32 



462.43*** 



.36 



Intercept 1.16 .02 

Black .09 .04 

Hispanic -.40 .06 

Intercept -1.16 .05 

Black .01 .03 

Hispanic -.23 .05 

Male -.21 .03 

Age M .00 

Married -.08 .03 

<High school .30 .03 

High school .OS .03 

Poor .82 m 

Middle Income .26 !03 

Intercept -1.41 .09 

Black -.09 .03 

Hispanic -.20 .04 

Male -.10 .03 

Age .04 .00 

Married -.13 .03 

<High school .22 .04 

High school .06 .03 

Poor .77 .04 

Middle Income .25 .03 

Body Mass Index .02 .00 

Smoking Index .00 .00 

Physical Activity -.63 .03 

Check Blood Pressure .41 .03 

Wear Seat Belt -.02 .03 

Intercept -1.41 .09 

Black -.16 .03 

Hispanic -.26 .05 

Male -.08 .03 

Age .03 .00 

Married -.09 .03 

<High school .16 .04 

High school .05 .03 

Poor .57 .04 

Middle Income .22 .03 

Body Mass Index .02 .00 

Smoking Index .00 .00 

Physical Activity -.61 .03 

Check Blood Pressure .40 .03 

Wear Seat Belt -.00 ,03 

Uninsured .09 .04 

Public .71 .04 

*p<.01, **p<.00I,***p<.000I. R2=Adjusted R-Square. Race, Education, Income, and Insurance coverage are dummy 
coded (l=variable, 0=no variable). Male (yes=I, no=0). Married (yes=l, no=0) Age, Body Mass Index, Smoking 
Index are treated as continuous variables. Physical Exercise, Check Blood Pressure, and Wear Seat belt are coded as 
l=yes, 0=no. See Table 21 for addition details on variable codes. 



.00 


65.90*** 


.02 


2.31 


-.06 


-7.14*** 


.00 


-23.43*** 


.00 


.34 


-.04 


-4.79*** 


-.06 


-6.88*** 


.46 


58.37*** 


-.02 


-2.58* 


.08 


8.75*** 


.02 


2.54* 


.19 


20.05*** 


.08 


8.75*** 


.00 


-16.54*** 


-.02 


-2.70* 


-.03 


-4.19*** 


-.03 


-3.35*** 


.39 


46.49*** 


-.04 


-4.17*** 


.06 


6.22*** 


.02 


1.94 


.18 


18.84*** 


.07 


8.34*** 


.07 


8.94*** 


.06 


7.61*** 


-.19 


-24.10*** 


.10 


13.11*** 


-.01 


-.85 


.00 


-16.46*** 


-.04 


-4.72*** 


-.04 


-5.28*** 


-.02 


-2.59* 


.39 


45.25*** 


-.03 


-3.00* 


.04 


4.43*** 


.01 


1.67 


.14 


13.24*** 


.07 


7.29*** 


.06 


8.32*** 


.06 


7.16*** 


-.18 


-23.12*** 


.09 


13.03*** 


-.00 


-.25 


.02 


2.23 


14 


16.69*** 



432.3* 



.38 



69 



Table 12. Lo gistic Regression Models Predicting Acute Symptoms 



Variable 



B 



SEB 



Chi-Square Odds Ratio 95%CI 



Step 1 



Step 2 



Step 3 



Step 4 



Intercept 


-.02 


.02 


Black 


.00 


.04 


Hispanic 


-.31 


.07 


Intercept 


-.80 


.07 


Black 


-.13 


.05 


Hispanic 


-.31 


.07 


Male 


-.48 


.05 


Age 


m 


.00 


Married 


.09 


.04 


<High school 


.21 


.05 


High school 


.12 


.05 


Poor 


.50 


.06 


Middle Income 


.23 


.04 


Intercept 


-.79 


M 


Black 


-.It 


.05 


Hispanic 


-m 


.08 


Male 


-.41 


.05 


Age 


.01 


.00 


Married 


.04 


.05 


<High School 


.10 


.05 


High School 


.06 


.05 


Poor 


.45 


.06 


Middle Income 


.20 


.05 


Overweight 


.29 


.05 


Ever Smoked 


.32 


.04 


Physical Activity 


-.40 


.04 


Check Blood Pressure 


.41 


.05 


Wear Seat Belt 


-.19 


.04 


Intercept 


-.84 


.09 


Black 


-.22 


.05 


Hispanic 


-.27 


.08 


Male 


-.40 


.05 


Age 


.01 


.00 


Not married 


.06 


.05 


<High school 


.07 


.06 


High school 


.06 


.05 


Poor 


.33 


.07 


Middle Income 


M 


.05 


Overweight 


n 


.05 


Ever Smoked 


.31 


.04 


Physical Activity 


-.39 


.04 


Check Blood Pressure 


.42 


.05 


Wear Seat Belt 


-.18 


.04 


Uninsured 


.14 


"m 


Public 


.36 


.07 



21.54**» 




.50 




.00 


1.00 


20.84*** 


.73 


625.19*** 




119.34*** 




6.65*** 


.88 


18.94*** 


.73 


101.46*** 


.62 


189,08*** 




4.45 


l.IO 


16.39*** 


1.23 


6.75* 


1,13 


68.98*** 


1.67 


25.76*** 


1.25 


880.78*** 




70.98 




12.39*** 


.83 


10.05** 


.79 


67,78*** 


.66 


96.79*** 




.82 


1.05 


3.56 


111 


1.84 


1.07 


50.75*** 


1.57 


19.09*** 


1.22 


36.86*** 


1.34 


62.56*** 


1.37 


98.82*** 


.67 


72.54*** 


1.51 


23.34*** 


.82 


910.79 




75.26 




17.16*** 


.80 


12.87** 


,76 


64.49*** 


,67 


93.39*** 




1,83 


1,07 


1.47 


1.07 


1.51 


1.06 


24.31*** 


1.39 


14.59*** 


1.19 


34.67*** 


1,33 


58.54*** 


1,36 


91.35*** 


,68 


73.30*** 


1.52 


20.92*** 


.83 


5.03 


1.16 


28.83*** 


1.44 



(.91-1.09) 
(.64-.84) 



(,79-,97) 
(,64-,84) 
(,56-,68) 

(1.00-1.21) 
(1.11-1.36) 
(1.03-1.23) 
(1.47-1.87) 
(1.14-1.37) 



(.75-.92) 
(.68-.9I) 
(.60-.73) 

(.95-1.15) 
(.99-1.23) 
(.97-1.17) 
(1.38-1.78) 
(1.12-1.34) 
(1.22-1.47) 
(1.27-1.49) 
(.62-.72) 
(1.38-1.66) 
(1.38-1.66) 



(.73-.90) 
(.66-.88) 
(.60-.74) 

(.97-1.77) 

(96-1,19) 

(,97-1,16) 

(1,22-1,59) 

(1,09-1.31) 

(1,21-1,46) 

(1,26-1,47) 

(,63-,74) 

(1,38-1.67) 

(.77-.90) 

(1.02-1.31) 

(1.26-1.64) 



*p<.01, **p<.001.***p<.0001, df =13. Percent concordant pairs for full model=65.7%, c=,66 
Race, Education, Income, and Insurance coverage are dummy coded ( l=variable, 0=no variable), Gender(Male=l, 
Female=0), Age, Body Mass Index, Smoking Index are treated as continuous variables. Physical Exercise, Check 
Blood Pressure, and Wear Seat belt are codes as l=yes, 0=no. 



70 



Table 13. Logistic Regression Models Predicting Chronic Symptoms 



Variable 



B 



SEB 



Chi-Square 



Odds Ratio 



95%CI 



Step 1 



Step 2 



Step 3 



Step 4 



Intercept 

Black 

Hispanic 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High School 

High School 

Poor 

Middle Income 

Overweight 

Ever Smoked 

Physical Activity' 

Check Blood Pressure 

Wear Seat Belt 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 

Overweight 

Ever Smoked 

Physical Activity 

Check Blood Pressure 

Wear Seat Belt 

Uninsured 

Public 







32.56*»* 






-.02 


.02 


1.38 






.06 


.05 


1.75 


1.06 


(.97-1.16) 


-.36 


.07 


27.69*** 
379.59*** 


.69 


(.61-.79) 


-.17 


.07 


5.35*** 






-.10 


.05 


4.44*** 


.90 


(.82-.99) 


-.46 


.07 


41.18*** 


.63 


(.55-.73) 


-.55 


.05 


138.05*** 


.58 


(.53-.63) 


.00 


.00 


6.87*** 






.21 


.05 


19.88 


1.23 


(1.12-1.35) 


.23 


.05 


20.62*** 


1.26 


(1.14-1.39) 


.07 


.05 


2.43* 


1.07 


(.98-1.17) 


.46 


.06 


58.68*** 


1.59 


(1.41-1.78) 


.17 


.04 


14.89*** 
529.53*** 


1.19 


(1.09-1.29) 


-.24 


.09 


6.58*** 






-.14 


.05 


7.69*** 


.87 


(.78-96) 


-.42 


.07 


32.07** 


.65 


(.57-.76) 


-.49 


.05 


98.19*** 


.61 


(.56-.66) 


.00 


.00 


.07*** 






.16 


.05 


10.73 


1.17 


(1.07-1.29) 


.19 


.05 


12.01 


1.20 


(.1.08-1.34) 


.04 


.05 


.75 


1.04 


(.95-1.14) 


.46 


.06 


50.69*** 


1.56 


(1.38-1.77) 


.15 


.05 


11.56*** 


1.17 


(1.07-1.28) 


.14 


.05 


8.39*** 


1.15 


(1.05-1.26) 


.19 


.04 


23.08*** 


1.21 


(1.12-1.31) 


-.27 


.04 


44.51*** 


.77 


(.71-.83) 


.43 


.05 


78.73*** 


1.53 


(1.39-1,68) 


-.09 


.04 


6.17*** 
542.73*** 


.91 


(.84-.98) 


-.25 


.09 


6.78 






-.17 


.05 


10.23*** 


.85 


(.77-.94) 


-.44 


.08 


34.39** 


.64 


(.55-.75) 


-.48 


.05 


94.89*** 


.62 


(.56-.68) 


.00 


.00 


.21*** 






.17 


.05 


12.49 


1.19 


(I.08-1.3I) 


.17 


.05 


9.36 


1. 18 


(I.06-I.3I) 


.04 


.05 


.67 


1.04 


(.95-1.14) 


.38 


.07 


32.52*** 


1.46 


(1.28-1.67) 


.14 


.05 


9.82*** 


1.15 


(1.06-1.26) 


.13 


.05 


7.61*** 


1.14 


(1.04-1.25) 


.18 


.04 


21.76*** 


1.20 


(1.11-1.29) 


-.26 


.04 


40.73*** 


.77 


(.72-.84) 


.42 


.05 


77.34*** 


1.53 


(1.39-1.69) 


-.09 


.04 


5.48*** 


.91 


(.84-.99) 


.03 


.06 


.26 


1.03 


(.91-1.17) 


.24 


.07 


12.99*** 


1.27 


(1.11-1.44) 



p<.01. **p<.00I,***p<.0001. df =13. Percent concordant pairs for full model=65.7%. c=.66. Race Education 
Income, and Insurance coverage are dummy coded (l=variable, 0=no variable). Male (yes=l. no=0) Married (yes=l 
no-0) Age, Body Mass Index, Smoking Index are treated as continuous variables. Physical Exercise Check Blood 
Pressure, and Wear Seat belt are coded as l=yes, 0=no. See Table 21 for addition details on variable codes 



71 



Table 14. Logistic Regression Models Predictine Medical Conditions 



Variable 



SEB 



Chi-Square Odds Ratio 95%CI 



Step I 



Intercept 


-.18 


.02 


Blaclc 


.06 


.05 


Hispanic 


-.64 


.07 


Step 2 






Intercept 


-3.59 


.09 


Black 


.24 


.06 


Hispanic 


-.35 


.08 


Male 


-.18 


.06 


Age 


.06 


.00 


Married 


.10 


.05 


<High school 


.14 


.06 


High school 


.12 


.05 


Poor 


.52 


.07 


Middle Income 


.07 


.05 


Step 3 






Intercept 


-4.58 


.12 


Black 


.13 


.06 


Hispanic 


-.33 


.09 


Male 


-.05 


.06 


Age 


.06 


.00 


Married 


.01 


.06 


<High School 


.09 


.06 


High School 


.11 


.05 


Poor 


.50 


.07 


Middle Income 


.08 


.05 


Overweight 


.90 


.05 


Ever Smoked 


.27 


.05 


Physical Activity 


-.28 


.05 


Check Blood Pressure 


1. 12 


.06 


Wear Seat Belt 


.00 


.05 


Step 4 






Intercept 


-4.59 


.12 


Black 


.10 


.06 


Hispanic 


-.34 


.09 


Male 


-.04 


.06 


Age 


.06 


.00 


Married 


.03 


.06 


<High school 


.07 


.06 


High school 


.11 


.06 


Poor 


.41 


.08 


Middle Income 


.07 


.05 


Overweight 


.89 


.05 


Ever Smoked 


.26 


.05 


Physical Activity 


-.26 


.05 


Check Blood Pressure 


I.I2 


.06 


Wear Seat Belt 


.00 


.05 


Uninsured 


.02 


.08 


Public 


.30 


.08 



90.49*** 
71.56*** 
1.57 

79.04*** 
3469.71*** 
1579.05*** 
19.40*** 
17.07*** 
10.80** 
2203.88*** 
3.41 
5.49 
5.56 

56.75*** 
2.17 

4000.82*** 
1399.49*** 
4.51 
12.68** 
.74 

1779.26*** 
.04 
1.99 
4.25 

45.30*** 
2.42 

273.05*** 
32.48*** 
35.23*** 
338.12*** 

.00 
4016.57*** 
1363.82*** 
2.85 
14.04** 

.49 

1696.77*** 

.25 

1.07 

4.03 

27.32*** 

1.66 
267.94*** 
30.57*** 
31.47*** 
333.02*** 
.03 
.05 
14.94*** 



1.06 

.52 



1.28 

.70 

.83 

1. 10 
1.15 
1.13 
1.69 

1.07 



1.14 

.72 
.95 

1.01 
1.09 
1.12 
1.65 
1.09 
2.47 

1.31 
.76 

3.06 
1.00 



III 

.71 

.96 

1.03 
1.07 
1.12 
1.52 
1.07 
2.45 
1.29 
.77 
3.05 
1.01 
1.02 
1.35 



(.97-1.16) 
(.45-.60) 



(1.15-1.42) 

(.60-83) 

(..75-.93) 

(.99-1.23) 
(1.02-1.28) 
(1.02-1.25) 
(1.47-1.93) 
(.98-1.19) 



(1.01-1.28) 

(.60-.86) 

(.84-1.07) 

(.90-1.14) 
(.96-1.24) 

(1.01-1.25) 
(1.43-I.9I) 
(.97-1.21) 
(2.22-2.75) 
(1.19-1.44) 
(.69-.83) 
(2.71-3.45) 

(.91-1.09) 



(.98-1.24) 

(.59-.84) 

(..85-1.08) 

(.92-1.15) 
(.94-1.21) 
(.1.00-1.25) 
(1.29-1.78) 
(.96-1.19) 
(2.19-2.73) 
(1. 18-1.43) 

(.70-.84) 

(2.71-3.44) 

(..92-1. 10) 

(.87-1.19) 

(1. 16-1.58) 



*p<.01, **p<.00l,***p<.000l, df =13. Percent concordant pairs for full model=65.7%, c=,66. Race, Education 
Income, and Insurance coverage are dummy coded (l=variable, 0=no variable), Male (yes=I, no=0). Married (yes=l 
no-0) Age. Body Mass Index, Smoking Index are treated as continuous variables. Physical Exercise Check Blood 
Pressure, and Wear Seat belt are coded as I=yes. 0=no. See Appendix A for addition details on variable codes 



72 

Overall, being White, older, poor, weighing and smoking more, having had blood 
pressure checked in past year, and being publicly insured were associated with increased 
odds of reporting medical conditions. 

Supplemental Analysis 

Multiple and logistic regression analysis was used to examine the relationship 
between race/ethnicity and health insurance coverage as well as health related behaviors 
while controlling for socio-demographic variables. 
Insurance Coverage 

, Table 15 presents secondary logistic regression analysis looking at the 
relationship between insurance coverage and demographic variables as depicted in Figure 
2. Race/ethnicity was entered in the first step of the model predicting private insurance 
coverage. Blacks and Hispanics were significantly less likely to possess private 
insurance compared to Whites (odds ratio: .33 and .31, respectively). Addition of socio- 
demographic variables reduced the Black and White effect, although they remained 
significant in the model (odds ratio:. 57 and .45, respectively). Overall, possession of 
private insurance was associated with being white, older, married, having greater than a 
high school education, and having high income. 

For the model predicting public insurance coverage. Blacks (266%) and Hispanics 
(124%) were significantly more likely to possess public insurance compared to Whites. 
Addition of socio-demographic variables to the model reduced the effect for both Blacks 
and Whites although they remained significant in the model (odds ratio: 2.48 and 1.95, 
respectively). Possession of public insurance was significantly associated with being 



73 

Black or Hispanic, female, older, not married, having a high school or less than high 
school education, and being poor or having middle income. 

Hispanics were 192% more likely to be uninsured compared to Whites. Blacks 
were also significantly more likely to be uninsured compared to Whites (odds ratio: 1.56). 
Addition of socio-demographic variables to the model eliminated the effect for Blacks 
and reduced the effect for Hispanics (odds ratio: 99 and 1.78, respectively). Being 
uninsured was significantly associated with being male, not married, having a high school 
or less than high school education, and having lower income. 
Body Mass Index 

Table 16 presents summary of regression analysis for Body Mass Index. 
Race/ethnicity was entered in the first step of the model and accounted for 1% of the 
variance. Blacks and Hispanics were significantly more likely to report a higher body 
mass compared to Whites. Blacks also had a significantly higher body mass index 
compared to Hispanics (AB=1 .49-.76, t=7.14). Addition of socio-demographic variables 
to the model increased the amount of variance explained by 2% and slightly increased the 
both the Hispanic and Black effect. Being older, married, and having a lower education 
were associated with an increased body mass index. Interestingly, neither gender nor 
income had significant effects on the model. 

Smoking Index 

-:/■■?. ; T 

Table 17 presents regression results for Smoking Index. In the first step of the 
model with race/ethnicity alone. Blacks and Hispanics reported smoking significantly less 
cigarettes compared to Whites. Controlling for socio-demographic variables in the 



74 

second step of the model reduced the effects of being Black by 27% and Hispanic by 
18%, suggesting that at least some of the differences across racial/ethnic groups in 
smoking behavior may be mediated by differences in across groups in various socio- 
demographic characteristics. There was a significant effect for both gender and age with 
males and older individuals smoking more than females and younger individuals. In 
addition, individuals with less education (less than high school: P==.07, high school: 
p=.08) were significantly more likely to have a higher smoking index compared to 
individuals more education (greater than high school). There was no significant effects 
for marital status and income. 
Physical Activity 

Results from logistic model predicting physical activity are presented in Table 18. 
For the first step, Blacks and Hispanics were significantly less likely to engage in regular 
physical activity (odds ratio: .57 and .69, respectively) compared to Whites. Socio- 
demographic variables were added in the second step of the model and resulted in a slight 
reduction in the Black effect, although it remained highly significant. Males were 
approximately 56% more likely to engage in regular physical activity than females. 
Likewise, younger individuals were more likely to engage in physical activity than older 
individuals. While there was no significant difference between individuals with a high 
school education and those with more than a high school education, individual with less 
than a high school education were almost 40% less likely to engage in regular physical 
exercise than those with more than a high school education. Compared to high income 



75 

individuals, poor individuals were 35% less likely to exercise while middle income 
individuals were 1 3% less likely to exercise. 
Blood Pressure Check 

Table 19 presents results for logistic model predicting Blood Pressure Check. For 
the first step of the model, Hispanics were 45% less likely to have had their blood 
pressure checked within the past year compared to Whites. Interestingly, there was no 
significant difference between Whites and Blacks. The significant effect found for 
Hispanics might be due to that fact that Hispanics were overall the healthiest group in the 
sample and therefore would be less likely to have visited a health professional and have 
blood pressure checked. On the other hand, Hispanics in the sample had the largest 
proportion of uninsured individuals. 

In Step 2 of the model, socio-demographic variables were added and resulted in a 
reducfion in the Hispanic effect by 34%, although it remained highly significant. Males 
were 53% less likely than females to have had their blood pressure checked while married 
individuals were 28% more likely to have had their blood pressure checked. Compared 
to individuals with high incomes, poor and middle income individuals were 29% and 
19% less likely to have their blood pressure checked, respectively. 
Wear Seat-Belts 

Table 20 presents results for logistic regression predicting the use of Seat-belts. 
In the first step with race/ethnicity alone. Blacks were 32% less likely than Whites to 
wear their seat-belts. There were no differences between Hispanics and White. Addition 
of socio-demographic variables to second step in the model reduced the Black effect by 



76 

54%, although it remained significant in the model. There was a significant effect for 
both gender and age with males being 28% less likely to wear seat-belts than females and 
younger individuals also being less likely to wear seat-belts. Interestingly, individuals 
with less than a high school education were 64% less likely to wear seat belts compared 
to individuals with more than a high school education. Likewise, compared to those with 
post secondary education, high school graduates were 48% less likely to wear seat belts. 
Poor individuals were 34% less likely to wear seat-belt compared to high income 
individuals while middle income individuals were 19% less likely to wear seat-belts 
compared to high income individuals. 



77 



Table 15. Summary of Hierarchical Regression Analysis for Variables 
Predicting Health Insurance Coyerag e 



Variables 



SEB 



Chi-square 



Odds Ratio 95%CI 



Private 



Step 1 



Step 2 



Intercept 

Black 

Hispanic 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 



1.41 


.03 


2786 


■I.ll 


.05 


504.7 


-1.16 


.07 


276.8 


1.90 


.11 


321.5 


-.57 


.06 


95.7 


-.79 


.09 


94.8 


-.05 


.06 


.76 


.02 


.00 


203.3 


.58 


.06 


93.7 


-1.04 


.07 


238.5 


-.39 


.06 


36.1 


-2.75 


.08 


1049.7 


-1.19 


.08 


250.8 



.33 


(.30-.36) 


.31 


(.27-.36) 


.57 


(.51-.64) 


.45 


(.37-.53) 


.95 


(.84-1.07) 


1.79 


(1.59-2.02) 


.35 


(.31-.40) 


.68 


(.59-77) 


.06 


(,05-.08) 


.30 


(.26-.35) 



Public 



Step 1 



Step 2 



Intercept 

Black 

Hispanic 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 



-2.28 


.04 


3894.6 


1.29 


.06 


486.2 


.80 


.09 


79.8 


-4.58 


.16 


827.2 


.91 


.07 


174.6 


.67 


.10 


41.71 


-.36 


.07 


21.33 


.02 


,00 


101.14 


-.58 


.08 


48.5 


.96 


.09 


118.2 


.33 


.09 


13.2 


2.45 


.12 


388.5 


1.17 


.12 


93.75 



3.66 


(3.26-4.11) 


2.24 


(1.87-2.67) 


2.48 


(2.17-2.84) 


1.95 


(1.59-2.39) 


.70 


(.60-.81) 


.56 


(.48-.66) 


2.62 


(2.20-3.12) 


1.40 


(1.17-1.69) 


11.57 


(9.07-14.76) 


3.22 


(2.53-4.08) 



Uninsured 



Step 1 



Step 2 



Intercept 

Black 

Hispanic 

Intercept 

Black 

Hispanic 

Male 

Age 

Married 

<High school 

High school 

Poor 

Middle Income 



2.15 


.03 


3844.5 


.45 


.07 


45.2 


1.07 


.08 


172.9 


1.37 


.13 


112.7 


-.01 


.07 


.02 


.58 


.09 


41.25 


.44 


.07 


37.6 


-.05 


.00 


646.28 


-.28 


.07 


15,35 


.61 


.08 


54.6 


.32 


.08 


18.1 


2.03 


.11 


371.9 


1.12 


.09 


142,52 



1,56 


(1.37-1.78) 


2.92 


(2.49-3.42) 


.99 


(.86-1.14) 


1.78 


(1.49-2.13) 


1.56 


(1.35-1.79) 


.76 


(.66-,87) 


1.84 


(1,57-2.17) 


1.38 


(1.19-1.60) 


7.59 


(6.18-9.33) 


3.06 


(2.54-3.67) 



*p<.01, **p<.001.***p<.0001. R2=Adjusted R-Square 

Race, Education, and Income are dummy coded (l=variable, 0=no variable). Male (yes=I, no=0). 

Insurance coverage - private (yes=l, no=0), public (yes=l. no=0), uninsured (yes=l, no=0) 



78 



Table 16. Summary of Hierarchical Regression Analysis for Variables 



Predicting Body Mass Index 


Variables 


B SEB 


P 


T Value F Value R2 


Stepl 








83.25*** .01 


Intercept 


24.84 


.05 


.00 


463.43*** 


Black 


1.49 


.12 


.12 


12.64*** 


Hispanic 


.74 


.17 


.04 


4.33*** 


Step 2 








39.38*** .03 


Intercept 


23.13 


.18 


.00 


128.54*** 


Black 


1.67 


.12 


.13 


13.67*** 


Hispanic 


.79 


.18 


.04 


4.50*** 


Male 


.24 


.12 


.02 


2.10 


Age 


.02 


00 


.07 


6.94*** 


Married 


.69 


11 


.07 


5.99*** 


<High school 


.67 


13 


.06 


5.28*** 


High school 


.43 


11 


.04 


3.85*** 


Poor 


.03 


14 


.00 


.25 


Middle Income 


-.19 


11 


-.02 


-1.83 



*p<.01, **p<.001,***p<.0001, R2=Adjusted R-Square 

Race, Education, and Income are dummy coded (l=variable, 0=no variable). 

Male (yes=l, no=0), Marital status (l=married, 0=not married) 

Body Mass Index - Continuous variable 



■J 



:■>'' , 



79 



Table 17. Summary of Hierarchical Regression Analysis for Variables 



Predicting Smoking Index 


Variables 


B 


SEB 


P 


T Value F Value R2 


Stepl 








142.36*** .02 


Intercept 


106699 


1685.49 


.00 


63.30*** 


Black 


-52167 


3720.79 


-.13 


-14.02*** 


Hispanic 


-61301 


5442.74 


-.10 


-11.26*** 


Step 2 








124.20*** .09 


Intercept 


-22279 


5512.60 


.00 


-4.04*** 


Black 


-38130 


3741.03 


-.08 


-10.19*** 


Hispanic 


-50295 


5390.38 


-.08 


-9.33*** 


Male 


48874 


3560.96 


.15 


13.73*** 


Age 


1587.94 


80.17 


.18 


19.81*** 


Married 


2624.42 


3516.86 


.01 


.75 


<High school 


25666 


3871.06 


.07 


6.63*** 


High school 


26712 


3433.27 


.08 


7.78*** 


Poor 


-2371.29 


4571.72 


-.01 


-.52 


Middle Income 


-2795.37 


3338.08 


-.01 


-.84 



*p<.01, **p<.001,***p<.0001, R2=Adjusted R-Square 

Race, Education, and Income dummy coded : (l=variable, 0=no variable) 

Male (yes=l, no=0) 

Marital status ( 1 =married, O=not married) 

Smoking Index - Continuous variable ., . 






■• ' i. 



!i' 



;• t 



;.;t /' M. ^r.^ : «. 



80 



Table 18. Logistic Regression Models Predicting Physical Activity 



Variable B SEB Chi-Square Odds Ratio 95%CI 



Step 1 154.5 



*** 



Intercept 


.16 


.02 


56.38*** 






Black 


-.55 


.05 


138.66*** 


.57 


(.53-.63) 


Hispanic 


-.36 


.07 


28.41*** 


.69 


(.61-79) 


Step 2 






1264.6*** 






Intercept 


1.23 


.08 


266.86*** 






Black 


-.44 


.05 


77.17*** 


.64 


(.58-.71) 


Hispanic 


-.39 


.07 


30.43*** 


.67 


(.58-77) 


Male 


.51 


.05 


110.98*** 


1.66 


(1.51-1.83) 


Age 


-.03 


.00 


380.25*** 






Married 


-.01 


.05 


.03 


.99 


(.90-1.09) 


<High school 


-.49 


.05 


90.66*** 


.61 


(.55-.67) 


High school 


-.05 


.05 


1.11 


.95 


(.87-1.04) 


Poor 


-.42 


.06 


45.60*** 


.66 


(.58-.74) 


Middle 


-.15 


.05 


10.29*** 


.87 


(.79-.94) 



*p<.01, **p<001,***p<.0001 

Race, Education, and Income dummy coded : (l=variable, 0=no variable). 

Male (yes=l, no=0) 

Marital status (l=married, 0=not married) 

Physical Activity (yes=l, no=0) 



81 



Table 19. Logistic Regression Models Predicting Blood Pressure Check 



Variable B S E B Chi-Square Odds Ratio 95%CI 



Step 1 67.84 



**♦ 



Intercept 


i.i7 


.03 


2208.39*** 






Black 


-.04 


.05 


.04 


.96 


(.86-1.07) 


Hispanic 


-.60 


.07 


71.09*** 


.55 


(.86-1.06) 


Step 2 






581.66*** 






Intercept 


.73 


.08 


73.02*** 






Black 


.02 


.06 


. .21 


1.02 


(.92-1.14) 


Hispanic 


-.40 


.07 


28.85*** 


.67 


(.57-.77) 


Male 


-.75 


.06 


184.02*** 


.47 


(.42-.53) 


Age 


.02 


.00 


263.11*** 






Married 


.30 


.05 


33.18*** 


1.35 


(1.22-1.50) 


<High school 


-.33 


.06 


31.19*** 


.72 


(.64-.81) 


High school 


-.20 


.05 


. 15.32*** 


.81 


(.73-.90) 


Poor 


-.18 


.07 


6.88* 


.82 


(.72-.96) 


Middle 


-.11 


.05 


4.32*** 


.89 


(.81-.99) 



*p<.01, **p<.001,***p<.0001. 

Race, Education, and Income are dummy coded: (l=variable, 0=no variable) 

Male (yes= 1 , no=0) 

Marital status (l==married, O=not married) 

Blood Pressure Check (yes=l, no=0) 



82 
Table 20. Lo gistic Regression Models Predicting Wearing Seat-belt 

Variable B SEB Chi-Square Odds Ratio 95%CI 



Stepl 65.49 



♦** 



Intercept 


.06 


.02 


9.13**** 






Black 


-.37 


.05 


64.77*** 


.68 


(.62-.75) 


Hispanic 


-.10 


.07 


2.24 


.90 


(.79-1.02) 


Step 2 






709.39*** 






Intercept 


.30 


.07 


18.58*** 






Black 


-.17 


.05 


11.92*** 


.84 


(.76-.93) 


Hispanic 


.20 


.07 


8.19* 


1.22 


(1.07-1.41) 


Male 


-.33 


.05 


47.83*** 


.72 


(.65-.79) 


Age 


.00 


.00 


83.08** 






Married 


-.13 


.04 


9.97* 


.88 


(.82-.95) 


<High school 


-1.03 


.05 


391.15*** 


.36 


(.32-.39) 


High school 


-.66 


.05 


208.03 


.52 


(.47-.57) 


Poor 


-.42 


.06 


47.24*** 


.66 


(.59-.74) 


Middle 


-.20 


.04 


21.59*** 


.81 


(.74-.89) 



*p<.01, **p<.001,***p<.0001. 

Race, Education, and are dummy coded: (l=variable, 0=no variable) 

Male (yes=l, no=0) 

Marital status (l=married, O^not married) 

Wear Seat Beh (yes=l, no=0) 



i"Si 



83 



Table 21. Summary of Variables 
Independent Variables 

Race 

Black 
White 
Hispanic 

Socio-demographic 

Gender 

Age 

Marital 

Education 

Income 



Measurement 



1 =yes, 0=no 
1 =yes, 0=no 
1 =yes, 0=no 





l=Male, 0=Female 




Actual age - continuous 


Status 


1 =married, O=not married 


on 


<High school (l=yes, 0=no) 


i. 


High school (l=yes, 0=no) 




>High school (l=yes, 0=no) 


High 


1 =yes, 0=no 


Middle 


l=yes, 0=no 


Low 


l=yes, 0=no 



Health Related Behaviors 

Body Mass Index 
Smoking Index 
Overweight 
Ever Smoked 
Physical Exercise 
Blood Pressure Check 
Wear Seat Belt 

Insurance Coverage 



Range (8-1 14) continuous 

Range (0-1569500) continuous 

1 =yes, 0=no 

1 =yes, 0=no 

1 =yes, 0=no 

l=yes, 0=no 

1 =yes, 0=no 



Private 


1 =yes, 0=no 


Public 


1 =yes, 0=no 


Uninsured 


l=yes, 0=no 


Dependent Variables 


Measurement 


Overall Health Rating 


l=Excellent 




2=Good 




3=Fair 




4=Poor r rt\ 

,' * J -■ 



Role Functioning 
Physical Functioning 
Acute Symptoms Scale 
Chronic Symptoms Scale 
Medical Conditions Scale 



n- 



\^- 



Range (0-2) Lower score - poorer functioning 
Range (0-5) Lower score - poorer functioning 
1 = 1 or more symptoms, 0=no symptoms 
1 = 1 or more symptoms, 0=no symptoms 
1 = 1 or more symptoms, 0=no symptoms 



CHAPTER 4 
DISCUSSION 

Epidemiological and health care research have long established the differential 
health status and increased mortality rates among minority groups in this country. 
However, the exact determinants of these differences remain unclear. Most of the 
research exploring this issue has focused primarily on the influence of SES as an 
explanatory variable. Numerous studies have confirmed the relationship between lower 
income levels and increased mortality and morbidity (i.e., Sorlie et al., 1992; Marmot & 
Smith, 1997; House et al.,1990; Ettner, 1996). However, as noted earlier, racial effects 
often persist even after controlling for SES. In addition, using such global indices as SES 
to explain differential health outcome across racial/ethnic groups may mask more direct 
and specific causes. Also, from a public policy perspective, while the elimination of 
poverty and economic disadvantage in this country presents an altruistic yet improbable 
task, the delineation of specific environmental and cultural factors that may contribute to 
the differential health outcome across racial and ethnic groups, over and above the effects 
of SES, may prove both tangible and worthwhile. For example, certain lifestyle 
behaviors that affect health (smoking, excessive alcohol consumption, being overweight) 
may reflect cultural styles and dietary patterns as well as socio-economic status (Lillie- 
Blanton et al., 1993). Research in the area of health, and particularly minority health, will 



84 



85 

need to focus on identifying the potential markers underlying racial differences in health 
outcome that extend beyond SES and those that are policy malleable. As Williams 
(1997) notes "when researchers identify social status differences in the distribution of a 
disease they should initiate a detailed examination of the contribution of environmental 
and genetic factors to observed differences. A broad range of factors intervene between 
race and health. These intervening mechanisms include health behavior; stress in family, 
occupational, and residential environments; social ties; psychological factors, including 
personality characteristics; culture; religious beliefs and behavior; and medical care." 

This present study sought to examine the influence of some of these intervening 
mechanisms in hopes of further enhancing what we currently know about the relationship 
between health status and race. Specifically, this study investigated the influence of 
socio-demographic variables, various health related behaviors, and health insurance 
coverage on health outcome across three racial/ethnic groups. 

Overall, the demographic characteristics of the present sample approximate 
findings from previous studies regarding differences in income and insurance coverage 
across racial/ethnic groups (Nickens, 1991; National Center for Health Statistics, 1994; 
Lillie-Blanton et al., 1993; Flack et al.,1995). Blacks were more likely to be poor and 
publicly insured while Whites were more likely to have high incomes and be privately 
insured. Hispanics had the highest rate of no insurance compared to Blacks or Whites. 
The effect of income on perceived health status found in this study is also consistent with 
earlier studies that reported individuals with higher SES to have better ratings of 
perceived health (i.e., Ettner, 1996; Lillie-Blanton et al., 1993; Ziff et al., 1995). 



86 

Relationship Between Race/Ethnicity and Health Related Behaviors. 

Descriptive analysis revealed varied results with regard to the relationship 
between race/ethnicity and health related behavior variables. For example, while Whites 
were more likely to engage in regular physical activity, they were also more likely to 
report having smoked and reported the highest number of cigarettes smoked a in lifetime. 
Likewise, while Blacks were more likely to report wearing seatbelts regularly, they were 
also more likely to weigh more and least likely to engage in physical activity. Results 
related to weight and physical exercise are relatively consistent with previous studies in 
this area. Both Black and Hispanic women have been found to have higher percentages 
of being overweight and to be less likely to report engaging in regular physical exercise 
compared to White women (CDC, 1994a; Durazo-Arvizu et al., 1997; Health People 
2000, 1991; Lillie-Blanton et al., 1993; Kumanyika, 1993; Myers et a!., 1995). 

As the model in Figure 2 depicts, socio-demographic variables may mediate the 
relationship between race/ethnicity and health related behaviors. The significant 
differences found across the three racial/ethnic groups on various socio-demographic 
dimensions provide some rationale for this contention. However, multivariate analysis 
found that Blacks and Hispanics were still significantly more likely to weigh more and be 
less physically active even after controlling for gender, age, marital status, education, and 
income level. Likewise, Whites remained significantly more likely to have smoked more 
cigarettes than Blacks or Hispanics after controlling socio-demographic variables. 
Previous research examining smoking prevalence across racial/ethnic group have found 
similar low rates of smoking among Hispanics (U.S. Department of Health and Human 



87 

*■ -•■■■>'■'■ 
4 ■ "^ 

Services, 1991). However, contrary to present results, Blacks have been found to have a 
higher prevalence of smoking compared to Whites (Myers et al., 1995; U.S. Department 
of Health and Human Services. 1991). Interestingly, while income did not emerge as a 
significant predictor of increased weight or smoking, having a high school education or 
less was significantly associated with increased weight and smoking compared to having 
more than a high school education. This finding has important policy implications in 
terms of which population may benefit more from health promotion campaigns aimed at 
reducing smoking behavior. 

Relationship Between Race/Ethnicity and Insurance Coverage 

Hispanics in this sample were more likely to be uninsured compared to Whites. 
This relationship persisted even after controlling for socio-demographic variables in 
multivariate analysis. This pattern is consistent with the literature on racial/ethnic 
differences in health insurance coverage (Nickens, 1991; Cornelius, 1993; Valdez et al, 
1993; Johnson et al., 1995). However, this finding must be considered cautiously due to 
possible subgroup differences in insurance coverage among Hispanics. For example, 
using the 1989 Current Population Survey, Trevino et al. (1992) found 16% of Puerto 
Ricans and 20% of Cuban Americans were uninsured compared to 37% of Mexican 
Americans. Cuban Americans were more likely to be privately insured while Puerto 
Ricans were more likely to be publicly insured. Amey et al. (1995) found similar patterns 
of insurance coverage for Puerto Rican, Cuban American, and Black families. However, 
they found Mexican Americans to be significantly less likely to have their entire family 
insured and more likely to have none of their family members insured. The greater 



88 

proportion of uninsured Mexican Americans compared to other Hispanic subgroups is 
presumably due to the fact that they are more Hkely to be undocumented and seasonal 
workers and therefore less likely to possess public or employer provided private 
insurance. In addition, Puerto Ricans are more likely to have households headed by 
single women and thus more likely to be covered by categorical public insurance 
programs such as Medicaid (Vega «&;Amaro, 1 994). 

Consistent with previous research. Blacks in this sample were more likely to be 
publicly insured compared to Whites. Inclusion of insurance to the model resulted in 
improved self-reported health status for Blacks on all dependent measures except Rate 
Health. Interestingly, possession of public insurance was significantly associated with 
poorer health status on all outcome measures included in this analysis. For this study, all 
types of public insurance were combined (low-income publicly insured (i.e., AFDC), 
Medicare, and those who qualify for Medicaid due to poor health). As such, the 
categorical nature of the program may have self-selected individuals with poor health. 
Using the same database, Short and Lair (1994) examined the health status of adults 
across five different insurance coverage options : privately insured with employment- 
related insurance; privately insured with non-group insurance; individuals who qualified 
for public insurance due to poor health; individuals who qualified for public insurance 
due of low income, and the uninsured. They found that individuals who were enrolled in 
the employer-sponsored plans were the healthiest, followed by individuals with private 
non-group insurance, the uninsured, the low-income publicly insured, and the publicly 
insured by virtue of poor health, respectively. Thus, while analyzing particular insurance 
groups separately allows for greater specificity, overall, the publicly insured (both low- 



89 

income and poor health eligible) appear to have the worst health status. It may be that 
public insurance represents a sort of catch net when all other options have been 
exhausted. For example, individuals whose health care costs may have exceeded their 
private insurance benefits or who have preexisting conditions that limit participation in 
private insurance programs may have to rely on public insurance when personal resources 
have been depleted. Likewise, individuals who are uninsured report overall better health 
status than those publicly insured. Given their health status, the uninsured may not see 
the value of purchasing health insurance. However, once faced with a medical illness and 
having depleted personal resources, these individuals ultimately may fall back on public 
insurance for coverage. 

Relationship Between Race/Ethnicity and Dependent Variables 

As noted earlier, the primary relationship of interest in this study is the effect of 
race/ethnicity on overall health status. On the outcome variables of Health Rating, Role 
Functioning, and Physical Functioning, Blacks reported the worst overall functioning 
while Hispanics reported the best. In addition, Hispanics acknowledged fewer Acute 
Symptoms, Chronic Symptoms, and Medical Conditions. Interestingly, Blacks and 
Whites were relatively similar in their report of symptoms on these three scales. 

One of the more fascinating findings of this study relates to the health status of 
Blacks. Descriptive analysis of the health status variables for Blacks indicate poorer self- 
ratings of overall health, physical functioning, and role functioning compared to both 
Whites and Hispanics. However, controlling for socio-demographic variables, health 



related behaviors, and insurance coverage reversed the Black effect incrementally to the 

• . : i 



^"^^y i \, > '. y^!^/^ { 



90 

point where Blacks actually reported better role and physical functioning than Whites. 
Thus, as hypothesized in proposed model, the indirect effect of these variables served to 
"distort" the true relationship between race/ethnicity and health status (i.e., converted a 
negative relationship into a positive relationship)(Rosenberg, 1968). To further illustrate 
this distorter effect, the relationship between race/ethnicity, insurance coverage, and the 
outcome variable Role Functioning will be briefly explored. Observing the relationship 
between race and Role Functioning alone, significantly more Blacks report that health 
limits their role functioning compared to Whites. However, when this relationship is 
broken down by insurance coverage a different relationship emerges. Specifically, 
significantly less Blacks report limitations in Role Functioning compared to Whites for 
those possessing private insurance (15% verse 22%); there is no significant difference for 
those possessing public insurance (51% verses 53%); and slightly more Whites report 
limits in Role Functioning compared to Blacks for those who are uninsured (1 5% verse 
20%)). However, since a larger proportion of Whites possess private insurance (80%) 
compared to Blacks (57%), their higher endorsement of role functioning limitation 
artificially elavates the sum total level of role functioning limitation. Once insurance 
coveraged is controlled for, the true relationship between race/ethnicity and role 
functioning emerges. 

Similarly, while there were no significant differences between Blacks and Whites 
on the measures of acute symptoms, chronic symptoms, and medical conditions, 
controlling for socio-demographic variables, health related behaviors, and insurance 
coverage resulted in Blacks reporting significantly less symptoms on all three scales 
compared to Whites. In this case, indirect model pathways of socio-demographic 



91 

variables, health related behaviors, and insurance coverage served to "suppress" or 
neutralize the true relationship between the independent and dependent variables 
(Rosenberg, 1968). For example, looking solely at the relationship between race and 
acute symptoms, an equal percentage of Blacks and Whites report experiencing acute 
symptoms (49.64% verses 49.62%, respectively). However, when broken down across 
each educational level, Whites consistently report more acute symptoms than Blacks. 
Analysis of frequency distributions indicate that individuals with less than a high school 
education are more likely to report acute symptoms and Blacks are more likely to have 
less than a high school education. Thus, the greater proportion of Blacks with less than a 
high school education suppresses the true relationship between race and the report of 
acute symptoms. Once education is controlled for (along with other control variables 
demonstrating similar relationships). Blacks are actually less likely to report acute 
symptoms than Whites. The Rate Health scale was the only outcome measure where the 
effect of being Black persisted after controlling for all other variables. 

These findings provide evidence supporting the model proposed in Figure 2. 
Socio-demographic variables accounted for the largest amount of variance in the model. 
For all six outcome variables, income and age (except for Chronic Symptom scale in the 
case of age) had the largest effect of socio-demographic variables. However, the addition 
of health related behaviors and insurance coverage to the models resulted in a reduction 
in the negative effect of being Black or an increase in the positive effect of being Black. 
This suggests that, as hypothesized, differences in health related behaviors and insurance 
coverage do in fact influence differential health outcome. 



92 

One perplexing finding that emerged in this study is the fact that while Hispanics 
were relatively similar to Blacks in the distribution of income and were actually less 
educated than Blacks, they had overall better functioning on all dependent variables 
measuring health status. This finding reflects the "epidemiological paradox" noted 
earlier: despite having similar SES as Blacks, the overall health status of Hispanics is 
better than Blacks and comparable to or even better than Whites. The exact mechanism 
driving this phenomenon is unclear. Some researchers have pointed to influence of the 
Hispanic culture and its role as a "buffer" against the negative effects often associated 
with poverty. For example, Hayes-Bautista ( 1 992) argues that the extension of the 
minority underclass model, a model delineating the vast negative social, environmental, 
and personal consequences of poverty, to Hispanics is inappropriate. He points to 
cultural factors such as healthy behaviors (characterized by less drinking, less smoking), 
more integrated family structure, and increased social support as protective factors 
against the deleterious consequences of poverty and the comparative better health status. 

The role of the Hispanic culture in moderating the negative health outcomes 
associated with underclass social conditions can also be examined by observing the effect 
of acculturation on health status. Gil, Vega, and Dimas (1994) define acculturation as 
"the process of cultural change due to two or more cultures coming into contact... (that) 
involves adoption of cultural beliefs, customs, behavior, and identity of an alternate 
culture". A number of studies have found increased acculturation to be associated with 
poorer health status and health related behaviors (Espino & Maldonado, 1 990; Vega & 
Amaro, 1994; Black & Markides, 1993). Vega and Amaro (1994) conducted a review of 
acculturation literature and found an overall deterioration in the health habits and health 



93 

status of Hispanics with increased length of stay in the United States. For example, they 
found increased acculturation to be positively associated with increased rates of infant 
mortality, low birth weigh, overall cancer rates, high blood pressure, adolescent 
pregnancy, alcohol consumption, and cigarette smoking. Using the Southwestern sample 
of the Hispanic Health and Nutrition Examination Survery (HHANES), Markides et al. 
(1990) found a significant association between alcohol consumption and level of 
acculturation. Amaro et al. (1990) also found acculturation into US society to be 
significantly associated with higher rates of illicit drug use (marijuana and cocaine) 
among Hispanics. Espino and Maldonado (1990) examined the relationship between 
acculturation and the prevalence of hypertension in Mexican Americans and found 
increased levels of acculturation to be associated with increases in the incidence of 
hypertension, even after controlling for age and gender. Finally, using a sample of Cuban 
American adolescent in Miami, Florida, Vega et al. (1993) found that family variables 
were primarily related to creating vulnerability to deviate behavior (i.e., development of 
attitudes favoring deviance). However, once vulnerability was created, they found 
acculturative conflicts played a stronger role in predicting actual delinquent behaviors. 

Based on a review of 30 publications examining the association between 
acculturation and mental health status, Rogler, Cortes, & Malgady (1991) propose that 
immigrants low in acculturation may experience lower self-esteem and symptomatic 
behavior due to being displaced from traditional social networks and having not yet 
identified with the host culture. Individuals high in acculturation are postulated to have 
negative mental health outcomes due to being alienated from traditional supportive 
networks and having internalized the host cultures values and norms, which may include 



94 

negative stereotypes and prejudices. Finally, the authors propose a curvilinear 
relationship between acculturation and mental health status, whereby individuals who are 
able to incorporate the ideology and participate in the host culture yet maintain their 
supportive traditional cultural ties experience positive mental and behavioral health. 
There does appear to be some evidence for this differential impact of acculturation. For 
example, increased acculturation has been associated with a decrease in prevalence of 
obesity and diabetes mellitus in Mexican Americans (Hazuda, Haffner, Stem, & Eifler, 
1988). Unfortunately, the extent to which acculturation may have influenced present 
results with respect to Hispanics can only be speculative. 

Limitations 

The limitations in this study are primarily related to the methodological issues 
noted earlier in the introduction. The most important limitation involves the inability to 
draw confident conclusions about the Hispanic population based on this analysis. As 
mentioned previously, significant differences exist across Hispanic subgroups in income 
level, educational attainment, insurance coverage, and health status. Combining 
subgroups may ultimately serve to wash out these differences and thus limit 
generalizability. 

Another limitation in this study is the use of self-report measures of health related 
behaviors and health status. Accuracy of information obtained from self-report measures 
maybe somewhat questionable. For example. Body Mass Index was calculated from self- 
reported height and weight information. The validity of this index relies heavily on the 
degree to which respondents accurately report their height and weight. Although, 



95 

Durazo-Arvizu et al. (1997) examined this issue by comparing self-reported and 
objectively measured BMI in 4,198 men and 4,686 women aged 30 and above from the 
NHANES II and found correlation coefficients for the two BMI's to be high (.92 and .95, 
respectively). In addition, the accuracy of information related to smoking behavior may 
be questionable as the index developed (total # of cigarettes smoked in lifetime and 
smoked over 100 cigarettes in lifetime (yes/no) are somewhat broad and rely on 
subjective report of past smoking history. 

The use of epidemiological survey data limits flexibility in the design of the study 
and the subsequent information obtained. For example, information regarding alcohol 
and drug consumption and dietary patterns as well as more specific questions related to 
physical activity and other preventative health behaviors might have offered additional 
information regarding the impact of health behaviors on health status. However, this 
information was not included in the survey and thus unavailable for inclusion in this 
study. 

Finally, the National Medical Expenditure Survey was conducted in 1987. As 
such, information obtained may be somewhat dated in its interpretive utility for 
evaluation of present day health status issues, thereby limiting generalizability to some 
degree. However, the primary interest of this study was to understand the relationship 
between health status and health related behaviors across racial/ethnic groups. While 
racial/ethnic differences in health behaviors and health outcome might have changed 
slightly since this data was collected, this should not affect the fundamental relationship 
between health behaviors and health status across groups. In addition, current literature 
appears to indicate that racial/ethnic differences in health status have not changed much 



96 

'• ■ \ -. V - ■ ' 

over the past 10 years (i.e., Lillie-Blanton, Martinez, Taylor, & Robinson, 1993; CDC, 
1994b; National Center for Health Statistics, 1995; Trentham-Dietz et al., 1997). For 
example, Meredith and Sui (1995) used the Medical Outcomes Study to examine 
variations in self-report health data for Asian and Pacific Islanders, Whites, African 
Americans, Latinos, and a group defined as "others". Consistent with present study, they 
found Latinos reported better physical functioning and slightly better role functioning 
compared to Whites and African Americans. Asian/Pacific Islander reported the best 
physical and role functioning while African Americans reported the worst across all 
groups. Likewise, distribution of insurance coverage across racial/ethnic groups has not 
changed much over time. Johnson et al. compared the reliability and validity of the 
Medical Outcomes Study Short-Form 36-ltem Health Survey (SF-36) in Black and White 
patients with acute chest pain. Insurance stratification by race was similar to present 
study (i.e.. Blacks more likely to be publicly insured while Whites were more likely to be 
privately insured). ' 

Finally, the influence of cultural differences across racial/ethnic groups in their 
report of symptoms is a potentially confounding area that is not addressed in this study. 
Indeed, there may be cultural differences across groups in the manner to which 
individuals acknowledge or endorse symptoms and in the way they approach medical 
treatment. 



97 

Future Research 

The present study provides additional insight into the dynamics of differential 
health status across racial/ethnic groups, particularly between Blacks and Whites. The 
results delineate factors apart from SES that influence health outcome; factors that may 
be more malleable to intervention. 

Future research in this area can build upon this study by addressing some of the 
methodological problems confronted during this study. For example, more objective 
measurements of health related behaviors and health status can be included in the model 
to address issues related to self-report bias. In addition, including other measures of 
health related behaviors such as alcohol and illicit drug consumption, dietary patterns 
(i.e., proportion of fiiiits and vegetables to carbohydrates, amount of meat), and a more 
refined measure of physical activity would provide a more complete picture of the 
manner in which specific behaviors influence health across minority groups. 

Finally, effective behavioral techniques and health promotion models will have to 
understand and address cultural issues in the context of race and ethnicity. For example, 
research examining cultural attitudes and beliefs regarding body size and excess weight 
among Blacks and Hispanics might point to effective ways of modifying behaviors in a 
culturally sensitive manner. As such, including in the model an assessment of the degree 
to which culture affects individual health related behaviors may provide additional insight 
on the relationship between racial/ethnic group membership and health status. 

The face of health care is changing rapidly. The burgeoning costs associated with 
present day medical care and the financing schemes developed to address these problems 






98 

have forced a focus on the behavioral correlates of health and increased interest in the 
cost effectiveness of preventative rather than curative health care. Fortunately, the 
profession of psychology has been studying and utilizing a variety of behavioral 
techniques to address medical problems (i.e, such as obesity, hypertension, headaches, 
and sleep disorders). As such, the role of psychology in general, and clinical psychology 
in particular, will increase as we progress away from disease treatment, toward disease 
prevention and overall health promotion. Hopefully, psychology will continue to play a 
critical role in the research, development, and implementation of clinical mechanisms to 
address the behavioral collorates associated with disease and illness. In addition, given 
the positive impact of preventative health behaviors on overall health status, health care 
professionals should aggressively promote positive health behaviors in their interactions 
with patients, particularly minority patients. Public health programs and campaigns 
should also direct more resources to increasing awareness and providing appropriate 
education to minority communities on the behavioral corrolates of disease while 
endorsing health promoting activities that are culturally sensitive (i.e., when working 
with African American or Hispanic families on issues related to diet, it is important to 
understand the role that food plays in the social dynamics of these cultures). 






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BIOGRAPHICAL SKETCH 

Delia F. Olufokunbi was bom in 1 969 in New York City, New York. She spent 
part of her childhood and early adolescence in Hawaii and in Nigeria, her father's 
homeland. She attended the University of Connecticut, receiving a Bachelor of Arts 
degree. While at "UCONN," she earned honors in psychology and graduated magna cum 
laude. She is currently completing her graduate education at the University of Florida, 
pursuing a doctoral degree in clinical and health psychology with an area of concentration 
in behavioral health policy. While pursuing her degree, Delia had a unique opportunity to 
participate in a year long legislative internship with the Florida House of Representatives 
Health Care Committee. Delia completed her clinical internship at the Louis De La Parte 
Florida Mental Health Institute and will soon be starting a two-year postdoctoral 
fellowship at Florida Mental Health Institute Department of Mental Health Law and 
Policy. The fellowship is part of the Public-Academic Fellows Program in Mental Health 
Services Research sponsored by the National Association of State Mental Health 
Directors Research Institute. Delia plans to pursue a career in behavioral health policy 
research and hopes to be involved in clinical work on a part time basis. 



108 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 





Suzanne B. Johnson, Chair 
Professor of Clinical and Health 
Psychology 

I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy 




Michael Miller 
Professor of Sociology 

I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 




Cynthia Belar 
Professor of Clinical and Health 
, . ' Psychology 

I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosbiphy. \ 

Duane Dede 
.' Assistant Professor of Clinical and 

Health Psychology 

I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosoph\ 




A A Mt^^ A - 



John C yddeback 

Prcrfes/or of Health Policy and 

Epidemiology 

<• -l '■ 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 




f>~^f=^ ^ 



Robert G. Frank .4 

Professor of Clinical and Health • . 
Psychology 

This dissertation was submitted to the Graduate Faculty of the College of Health 
Professions and to the Graduate School and was accepted as partial fulfillment of the 
requirements for the degree of Doctor of Philosophy. 



--^X/^'^^v 



December 1997 

Dean, College of Health Professions 



Dean, Graduate School 






.Si Ml) 



UNIVERSITY OF FLORIDA 



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